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Objective To analyze the application of the ARIMA and LSTM models in predicting pertussis incidence in U-rumqi, providing a basis for assessing the epidemic trend of pertussis. Methods Monthly reported incidence data of pertussis in Urumqi from 2011 to 2021 were used to establish ARIMA and LSTM models. The incidence data from 2022 to 2023 were utilized to validate the predictive performance of the two models. The modelsJ performance was evaluated using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), and the incidence of pertussis in 2024 was predicted. Results The incidence of pertussis in Urumqi from 2011 to 2023 showed an upward trend with seasonal variations. Additionally, a high incidence state of pertussis began in August 2023. Both the ARIMA and LSTM models demonstrated good fitting, although there were discrepancies in their predictions for July to December 2023. The overall predictive performance of the LSTM model (RMSE二32.34, MAE二 11.41) was superior to that of the ARIMA model (RMSE=42.81, MAE=14.34). The LSTM model, which showed better validation results, predicted a continued increase in pertussis incidence for 2024. Conclusion The LSTM model provides a more accurate prediction of the pertussis incidence trend in Urumqi, offering valuable insights for monitoring and controlling the epidemic of pertussis. [ABSTRACT FROM AUTHOR]

Background: Respiratory infectious diseases have the highest incidence among infectious diseases worldwide. Currently, global monitoring of respiratory pathogens primarily focuses on influenza and coronaviruses. This study included influenza and other common respiratory pathogens to establish a local respiratory pathogen spectrum. We investigated and analyzed the co-infection patterns of these pathogens and explored the impact of lifting non-pharmaceutical interventions (NPIs) on the transmission of influenza and other respiratory pathogens. Additionally, we used a predictive model for infectious diseases, utilizing the commonly used An autoregressive comprehensive moving average model (ARIMA), which can effectively forecast disease incidence. Methods: From June 2023 to February 2024, we collected influenza-like illness (ILI) cases weekly from the community in Xuanwu District, Nanjing, and obtained 2046 samples. We established a spectrum of respiratory pathogens in Nanjing and analysed the age distribution and clinical symptom distribution of various pathogens. We compared age, gender, symptom counts, and viral loads between individuals with co-infections and those with single infections. An autoregressive comprehensive moving average model (ARIMA) was constructed to predict the incidence of respiratory infectious diseases. Results: Among 2046 samples, the total detection rate of respiratory pathogen nucleic acids was 53.37% (1092/2046), with influenza A virus 479 cases (23.41%), influenza B virus 224 cases (10.95%), and HCoV 95 cases (4.64%) being predominant. Some pathogens were statistically significant in age and number of symptoms. The positive rate of mixed infections was 6.11% (125/2046). There was no significant difference in age or number of symptoms between co-infection and simple infection. After multiple iterative analyses, an ARIMA model (0,1,4), (0,0,0) was established as the optimal model, with an R2 value of 0.930, indicating good predictive performance. Conclusions: The spectrum of respiratory pathogens in Nanjing, Jiangsu Province, was complex in the past. The primary age groups of different viruses were different, causing various symptoms, and the co-infection of viruses did not correlate with the age and gender of patients. The ARIMA model estimated future incidence, which plateaued in subsequent months. [ABSTRACT FROM AUTHOR]

Background: The aim of this study is to analyze the trend of acute onset of chronic cor pulmonale at Chenggong Hospital of Kunming Yan'an Hospital between January 2018 and December 2022.Additionally, the study will compare the application of the ARIMA model and Holt-Winters model in predicting the number of chronic cor pulmonale cases. Methods: The data on chronic cor pulmonale cases from 2018 to 2022 were collected from the electronic medical records system of Chenggong Hospital of Kunming Yan'an Hospital. The ARIMA and Holt-Winters models were constructed using monthly case numbers from January 2018 to December 2022 as training data. The performance of the model was tested using the monthly number of cases from January 2023 to December 2023 as the test set. Results: The number of acute onset of chronic cor pulmonale in Chenggong Hospital of Kunming Yan'an Hospital exhibited a downward trend overall from 2018 to 2022. There were more cases in winter and spring, with peaks observed in November to December and January of the following year. The optimal ARIMA model was determined to be ARIMA (0,1,1) (0,1,1)12, while for the Holt-Winters model, the optimal choice was the Holt-Winters multiplicative model. It was found that the Holt-Winters multiplicative model yielded the lowest error. Conclusion: The Holt-Winters multiplicative model predicts better accuracy. The diagnosis of acute onset of chronic cor pulmonale is related to many risk factors, therefore, when using temporal models to fit and predict the data, we must consider such factors' influence and try to incorporate them into the models. [ABSTRACT FROM AUTHOR]

To address the problem of temperature errors in secondary instruments operating in high- and low-temperature environments, this paper proposed a temperature correction method based on the ARIMA-GM(1,1) model. First, a standard source was connected to a temperature secondary instrument placed in a high- and low-temperature circulation box. The errors between the measurements of the standard source and the secondary instrument could be calculated and obtained a set of error sequences. Second, the error sequences were used to establish an ARI MA model and obtained a set of predicted values. And the residual between the errors and the predicted values could be calculated. To improve the accuracy of the ARIMA model, a GMC(1,1) residual correction model was established based on the residual sequences. Lastly, the ARIMA and the GMC(1,1) models were combined to formulate an ARIMA-GM model that could perform error self-correction for the temperature secondary instrument. In application experiments, the model achieved smaller average relative errors than a traditional ARIMA and hybrid models. Finally, we developed the ARIMA-GM(1,1) model into a software and applied it to cases of actual detection. [ABSTRACT FROM AUTHOR]

This article examines the influence of specific time series attributes on the efficacy of fuel demand forecasting. By utilising autoregressive models and Markov chains, the research aims to determine the impact of these attributes on the effectiveness of specific models. The study also proposes modifications to these models to enhance their performance in the context of the fuel industry's unique fuel distribution. The research involves a comprehensive analysis, including identifying the impact of volatility, seasonality, trends, and sudden shocks within time series data on the suitability and accuracy of forecasting methods. The paper utilises ARIMA, SARIMA, and Markov chain models to assess their ability to integrate diverse time series features, improve forecast precision, and facilitate strategic logistical planning. The findings suggest that recognising and leveraging these time series characteristics can significantly enhance the management of fuel supplies, leading to reduced operational costs and environmental impacts. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to propose the use of time series autoregressive integrated moving average (ARIMA) models to predict gas path performance in aero engines. The gas path is a critical component of an aero engine and its performance is essential for safe and efficient operation of the engine. Design/methodology/approach: The study analyzes a data set of gas path performance parameters obtained from a fleet of aero engines. The data is preprocessed and then fitted to ARIMA models to predict the future values of the gas path performance parameters. The performance of the ARIMA models is evaluated using various statistical metrics such as mean absolute error, mean squared error and root mean squared error. The results show that the ARIMA models can accurately predict the gas path performance parameters in aero engines. Findings: The proposed methodology can be used for real-time monitoring and controlling the gas path performance parameters in aero engines, which can improve the safety and efficiency of the engines. Both the Box-Ljung test and the residual analysis were used to demonstrate that the models for both time series were adequate. Research limitations/implications: To determine whether or not the two series were stationary, the Augmented Dickey–Fuller unit root test was used in this study. The first-order ARIMA models were selected based on the observed autocorrelation function and partial autocorrelation function. Originality/value: Further, the authors find that the trend of predicted values and original values are similar and the error between them is small. [ABSTRACT FROM AUTHOR]

The sum total number of new family planning (KB) acceptors in Pamekasan Regency decreased from March to April 2020 due to the COVID-19 pandemic. This decline will hinder the increase in achieving the Modern Contraceptive Prevalence Rate (mCPR) as the target of the 2020-2024 National Medium-Term Development Plan (RPJMN), so modelling is needed to predict the sum total of new family planning acceptors in Pamekasan Regency. This research aims to predict the sum total of new family planning acceptors in Pamekasan Regency using the ARIMA model. This research is a non-reactive quantitative research. The unit of analysis for this research is all-new monthly family planning acceptors in Pamekasan Regency. The data used are the number of new monthly family planning acceptors from January 2016 to December 2021, sourced from the Pamekasan Regency Women's Empowerment, Child Protection and Family Planning Service. The research results show that the best model for predicting the number of new family planning acceptors in Pamekasan Regency is ARIMA [1,1,1] with the equation 0.0011(B)11Zt=0.006+0.0001(B)at. Prediction results using the ARIMA [1,1,1] model show that the number of new family planning acceptors tends to increase in January-December 2022. The sum of new family planning acceptors shows an increasing pattern, but the increase does not reflect the impact of the end of the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Transit crashes are a serious social problem for any country, with a significant loss of human lives and economic consequences that are difficult to quantify. This article proposes a characterization of the transit crash rate for urban road safety using time series. A quantitative descriptive study was conducted, characterizing the variables of each crash extracted from the National Traffic Agency of Ecuador (NTA); the data were processed at a descriptive and predictive level for the city of Guayaquil. The first step was an exploration of the scientific interest of the topic with the processing of bibliographic data taken from Scopus and Web of Science articles. Among the results obtained, there is a growing trend of research related to the evaluation of traffic crash through applied statistics. Every day, approximately 155 people die as a result of a traffic crash. In addition, traffic crashes are analyzed based on three indicators: number of crashes, injuries and onsite fatalities. Finally, an adequate performance is found, with very few differences in the forecast of incidents using three times series models, autoregressive integrated moving average (ARIMA). It is expected that this study will be valuable for data analysts and decision makers at the security level to reduce human losses related to these events in urban cities with similar characteristics to the analyzed cases. [ABSTRACT FROM AUTHOR]

It aims to improve the application efficiency of modern science and technology in farm operation, enhance the prediction effect of machine learning and blockchain technology on farm supply and demand issues, and help enterprise managers to better manage and operate enterprise projects. First, the agricultural super business model under the blockchain + Internet of Things (IoT) is analysed, and the farm management system of the IoT + blockchain is constructed. Then, the demand forecasting model of agricultural products is established. Autoregressive Integrated Moving Average model (ARIMA) and Support Vector Machine (SVM) models are used to predict demand. Finally, the characteristics of the two models in prediction are discussed. The results show that the ARIMA model can reach 99.2% in the application efficiency evaluation of farm operation. Moreover, in the case of proper selection of model indicators, the prediction result is close to the actual value, the prediction error fluctuation is small, and the maximum error value is 0.4318. The local error of SVM model in predicting the demand of farm supply chain is slightly larger, and the maximum error value is 8.5430. By comparing the error results of the two models, the prediction accuracy of ARIMA model is higher than that of SVM model. The ARIMA model designed in this work can provide effective product demand prediction for farm operation and provide a strong technical reference for farm operation. The research not only provides technical support for machine learning and blockchain technology to predict farm supply and demand, but also contributes to the integrated management and sustainable development of enterprises. [ABSTRACT FROM AUTHOR]

This project mainly focuses on the study of word guessing games, analyzing factors such as the popularity of the game, the playability of the game itself, the difficulty of the game itself, and the game effects of the participants. Firstly, analyze the useful attributes from the words and whether these attributes have an impact on the proportion of game score results reported on that day. Secondly, clustering models are used to automatically partition words, and the difficulty is divided into two levels: simple and difficult. The machine learning xgboost model is used to evaluate the model through confusion matrix, accuracy, recall, and F1 value. The study found that the total number of reports is consistent with the trend of the number of reports in hardcore mode. The popularity rapidly increased in the early stage and gradually decreased in the later stage, which means that the loyalty of players has basically been washed away, and the remaining are mostly people with great potential to continue playing. [ABSTRACT FROM AUTHOR]

Time series analysis (TSA) is a statistical approach rooted in data mining that measures outcomes that consistently fluctuate over time. It is uniquely positioned as a methodology for preventive intervention research due to its capacity to measure long‐term impact in natural settings. With the ability to detect temporal patterns in a series of chronological data points, it allows researchers to detect naturalistic patterns, forecast future observations, and explain a change in data after an intervention is put in place. However, its application is historically sparse across behavioral research. This article provides counseling researchers with an overview of TSA and presents a step‐by‐step guide for conducting TSA analyses in counseling research. The author discusses the key concepts, steps for implementation, and the strengths and challenges of conducting TSA. [ABSTRACT FROM AUTHOR]

In the dynamic environment of fresh food supermarkets, managing the short shelf life and varying quality of vegetable products presents significant challenges. This study focuses on optimizing restocking and pricing strategies to maximize profits while accommodating the diverse and time-sensitive nature of vegetable sales. We analyze historical sales, pricing data, and loss rates of six vegetable categories in Supermarket A from 1 July 2020 to 30 June 2023. Using advanced data analysis techniques like K-means++ clustering, non-normal distribution assessments, Spearman correlation coefficients, and heat maps, we uncover significant correlations between vegetable categories and their sales patterns. The research further explores the implications of cost-plus pricing, revealing a notable relationship between pricing strategies and sales volumes. By employing Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) models, we forecast sales and determine optimal restocking volumes. Additionally, we use price elasticity theories and a comprehensive model to predict net profit changes, aiming to enhance profit margins by 47%. The study also addresses space constraints in supermarkets by proposing an effective assortment of salable items and individual product restocking plans, based on FP-Growth algorithm analysis and market demand. Our findings offer insightful strategies for sustainable and economic growth in the supermarket industry, demonstrating the impact of data-driven decision-making on operational efficiency and profitability. [ABSTRACT FROM AUTHOR]

Biogas is a renewable energy source (RES). The aim of this research was to analyze the perspectives of electricity production from biogas in the European Union. The main source of information was data from Eurostat. We analyzed electricity production from biogas in the European Union (EU). The scope of this research was data from 2012 to 2021. First, we presented biogas production by feedstock type across the world. Then, we presented changes in electricity production from biogas in the EU. We used different methods to evaluate the changes in biogas production. First, we used the ARiMA (Autoregressive Moving Average) model to evaluate the stationarity of the time series. Our electricity production from biogas data proved to be stationary. Second, we elaborated on the prognosis of future changes in electricity production from biogas. The largest producer of biogas is the EU, and it is produced from crops, animal manure, and municipal solid waste. Our research found that the largest production from biogas in 2021 took place in Germany, Italy, and France. These countries have the greatest potential for electricity production from biogas, and they have spent significant funds on facilities and technology. Such countries as Ireland, Greece, Spain, France, Croatia, Poland, Portugal, Romania, Finland, and Sweden increased their electricity production from biogas in 2021 compared to 2020. According to our prognosis, the global production of biogas will increase from 62.300 TWh to 64.000 TWh in 2019–2026 (2.7% increase). In 2022–2026, such countries as Estonia (60.4%), Latvia (29.6%), Croatia (27.6%), Slovenia (10.9%), and Poland (8.2%) will increase their electricity production from biogas the most. In 2022–2026, such countries as Italy (0.68%), Portugal (1.1%), Greece (1.5%), Slovakia (2.3%), and Germany (2.6%) will increase their electricity production from biogas the least. Only Romania (−17.6%), Finland (−11.5%), Lithuania (−9.1%), and Malta (−1.06%) will decrease their production of electricity from biogas in 2022–2026. Such countries as Bulgaria (2344%), Denmark (590.9%), Croatia (449%), and France (183%) increased biogas consumption in 2013–2022. A decrease in the inland consumption of biogas in 2013–2022 was observed in Spain, Cyprus, Latvia, Luxembourg, Austria, and Slovenia. [ABSTRACT FROM AUTHOR]

Oil crops are considered important crops because the demand for them is derived from the demand of production and consumption of vegetable oils. This research aimed to identify the current and future status of vegetable oil consumption in Egypt by estimating the size of the vegetable oil gap and its future expectations to raise the efficiency of the economic and technical performance of vegetable oils in Egypt. This study relied on both descriptive and quantitative methods represented by averages, percentages, annual growth rate, and time trend equations for determining data and presenting its results. In addition, this research relied on estimating the ARIMA model to forecast the future of vegetable oil gap by relying on secondary data published by the Ministry of Agriculture and Land Reclamation, Economic Affairs Sector, and various issues of food balance bulletins during the period from (2007-2021). The most important results were reached that maize crop ranked the first place in both of production, followed by olives which ranked the second place, then the production of cotton seeds, peanut seeds and sesame seeds, while the lowest production of soybeans and sunflower seeds. While olive crop ranked first in the productivity, followed by maize in the second rank, then cotton seeds and sesame seeds were the least in productivity during the period (2007-2021). The quantity of soybean oil production ranked first and contributed the largest share in producing vegetable oils during the study period (2007-2021), followed by production quantity of cotton seed oil, then production quantity of sunflower oil and olive oil, while the production quantity of maize oil came in the last rank. The consumption quantity of palm oil ranked first that contributed with the largest share in consuming of vegetable oils during the study period (2007-2021), followed by the consumption quantity of soybean oil, then sunflower oil and cotton seed oil, while olive oil came in the last rank. By reviewing the results of forecasting of the food gap of vegetable oils, it is cleared that the increasing of gap between local production and national consumption is evident, which indicates the need for the state to make efforts to developed plans and programs to increase production from its various sources. Not only rationalizing consumption but also reducing the increase of vegetable oils gap in Egypt during the study period. [ABSTRACT FROM AUTHOR]

Background: Application of accumulated experience and management measures in the prevention and control of coronavirus disease 2019 (COVID-19) has generally depended on the subjective judgment of epidemic intensity, with the quality of prevention and control management being uneven. The present study was designed to develop a novel risk management system for COVID-19 infection in outpatients, with the ability to provide accurate and hierarchical control based on estimated risk of infection. Methods: Infection risk was estimated using an auto regressive integrated moving average model (ARIMA). Weekly surveillance data on influenza-like-illness (ILI) among outpatients at Xuanwu Hospital Capital Medical University and Baidu search data downloaded from the Baidu Index in 2021 and 22 were used to fit the ARIMA model. The ability of this model to estimate infection risk was evaluated by determining the mean absolute percentage error (MAPE), with a Delphi process used to build consensus on hierarchical infection control measures. COVID-19 control measures were selected by reviewing published regulations, papers and guidelines. Recommendations for surface sterilization and personal protection were determined for low and high risk periods, with these recommendations implemented based on predicted results. Results: The ARIMA model produced exact estimates for both the ILI and search engine data. The MAPEs of 20-week rolling forecasts for these datasets were 13.65% and 8.04%, respectively. Based on these two risk levels, the hierarchical infection prevention methods provided guidelines for personal protection and disinfection. Criteria were also established for upgrading or downgrading infection prevention strategies based on ARIMA results. Conclusion: These innovative methods, along with the ARIMA model, showed efficient infection protection for healthcare workers in close contact with COVID-19 infected patients, saving nearly 41% of the cost of maintaining high-level infection prevention measures and enhancing control of respiratory infections. [ABSTRACT FROM AUTHOR]

In order to improve the high accuracy and reliability of drone pitch angle fault data processing and prediction, and avoid increasing the cost of drone flight testing, long short term memory(LSTM), attention mechanism + LSTM model, and autoregressive integrated moving average model(ARIMA) model were used to predict pitch angle faults data in drone systems. The results show that for ARIMA, mean absolute error(MAE)is 0. 35, root mean square error(RMSE) is 0. 73, mean absolute percentage error (MAPE) is 23. 80% . For LSTM model, MAE = 0. 49, RMSE = 0. 74, MAPE = 45. 20% . For attention mechanism + LSTM model, MAE = 0. 17, RMSE = 0. 53, MAPE = 18. 93% . It can be seen that the attention mechanism + LSTM model is more suitable for predicting the pitch angle of flight tests data than the other two models. The above three methods have practical significance for predicting unmanned aerial vehicle faults. Effective prediction can promote the latest progress in anomaly detection of autonomous aircraft and mobile robots or foreign direct investment research, in order to further improve the safety of automatic and remote flight operations. [ABSTRACT FROM AUTHOR]

Objective To compare the fitting effect of ARIMA model and grey model (GM) (1,1) in the under five mortality rate (U5MR) in Shenzhen, and to predict the U5MR in Shenzhen in the next 3 years, so as to provide scientific basis for the formulation of child health care plan. Methods Taking the U5MR in Shenzhen from 2005 to 2022 as the original data, ARIMA model and GM (1,1) model were constructed to fit the child mortality rate. The mean absolute error (MAE) and mean absolute percentage error (MAPE) were used to compare the fitting effect of the two models. Results From 2005 to 2022, the U5MR in Shenzhen showed a downward trend, lower than the national average. The MAE of the constructed ARIMA model and GM (1,1) model was 030 and 0.24, respectively, and the MAPE was 11.53% and 8.73%, respectively. The fitting effect of the GM (1,1) model was good. The U5MR in Shenzhen from 2023 to 2025 would be 1.27 %-- 1.18 and 1.09 %-- respectively, predicted by GM (1,1) model. Conclusion The fitting effect of GM (1,1) model on U5MR in Shenzhen is better than that of ARIMA model, and it is predicted that the U5MR in Shenzhen will decrease year by year in the next 3 years. [ABSTRACT FROM AUTHOR]

In the present work, the Auto-Regressive Integrated Moving-Average (ARIMA) forecasting method is used to predict the remaining useful life (RUL) of an axial piston pump used in the hydraulic system applied to the sheet metal industry. The leakage volume from the piston pump is considered a significant parameter for the life prediction analysis using the ARIMA model, as the proposed model uses auto-regressive (AR) and moving-average (MA) modelling for forecasting with high prediction accuracy. The life estimation through the ARIMA model is based on the degradation method of remaining useful life prediction. The life prediction results were in an acceptable range with a forecasting root-mean-square error (RMSE) of 0.3. The investigation results concluded that out of the two forecasting models, i.e., ARIMA (1,1,2) and ARIMA (20,1,2), ARIMA (1,1,2) model has the least forecasting error and also shows better adjustment with the historical data. The RUL of the hydraulic pump considered in the present investigation is predicted to be 28 months. Additionally, the model developed can be used to determine the lifetime of the operational pump and plan its maintenance schedule for hydraulic-operated unmanned vehicles used for off-road applications like mining, agriculture, defence, etc. The proposed RUL forecasting technique can help avoid mechanical systems from uncertain breakdowns and will also play a pivotal role in maintenance. [ABSTRACT FROM AUTHOR]

In recent years, under the influence of changes in natural conditions and human social activities, the issue of global warming has become increasingly prominent. So it is crucial to effectively predict the future trend of temperature changes. In this regard, from the perspective of statistical models, this paper studies a new combination model, namely the new GM-ARIMA model, based on linear combination of weight calculation. Furthermore, it also analyzes the prediction effect through comparative experiments and uses multiple performance evaluation indicators, so as to prove the scientificity and effectiveness of the proposed combination model in this paper. Finally, according to the experimental results, it can be clearly found that among the four methods for calculating the weights of linear combination, namely the equal weight method, the variance reciprocal method, the residual reciprocal method and the standard deviation method, the combination model using the standard deviation method for calculation has the highest prediction accuracy, so it is finally decided to use this method to build the combination model (namely S-GM-ARIMA). In addition, the experimental results show that the S-GM-ARIMA model achieves the best prediction results compared to other existing prediction models. Among them, the MAE of S-GM-ARIMA decreases by 10.38% and 16.22% compared to the GM(1,1) model and ARIMA model, respectively. The RMSE of S-GM-ARIMA decreases by 4.52% and 10.03% compared to the GM(1,1) model and ARIMA model, respectively. And the MAPE of S-GM-ARIMA decreases by 10.34% and 16.17% compared to the GM(1,1) model and ARIMA model, respectively. Therefore, the new GM-ARIMA combination model studied in this paper has relatively higher prediction accuracy when making predictions, and can be used to make more effective and accurate predictions of global average temperature. This study can also provide reference for countries in making decisions to address global warming issues. [ABSTRACT FROM AUTHOR]

Simple Summary: The Japanese eel, a high-valued commercial fish species in the Yangtze River ecosystem, faces a significant decline in the resource. In the present study, we reported our 11-year investigation to track the migratory patterns of glass eels of Anguilla japonica into the Yangtze estuary. We identified two distinct annual peak arrival times which coincide with cooler water temperatures, i.e., the first peak at 6–8 °C and the second at 10–12 °C, both in sync with the strong spring tides of the lunar cycle. Despite rebounded population in a few years, an auto-regressive integrated moving average model analysis reveals a concerning trend: since 2016, rising temperatures have been associated with delayed recruitment. This change poses a threat to their migratory routes and life cycle stability. Underlining the critical nature of our research, our findings call for immediate conservation efforts to counteract the impact of climate change on the eel and to safeguard the biodiversity of the river ecosystem, a valuable asset to society. The decline of Japanese eel (Anguilla japonica) populations in the Yangtze River estuary represents a critical conservation concern. Eleven-years of daily catch data during recruitment periods (i.e., January–April, 2012–2022) indicate that annual catch averaged from 153 to 1108 eels, and show a bimodal pattern in glass eel arrivals. Utilizing seasonal-trend decomposition and generalized additive models, we demonstrated a strong correlation between catch abundance, optimal water temperatures, and lunar cycles. An auto-regressive integrated moving average (ARIMA) model predicts an increase in glass eel numbers for 2023–2024 but also points to a concerning trend of delayed recruitment timing since 2016, attributable to the 0.48 °C per decade rise in sea surface temperatures. This delay correlates with a significant decrease in the average body weight of glass eels, suggesting potential energy deficits that may hinder successful upstream migration. This study not only furthers our understanding of glass eel recruitment dynamics but also underscores the urgent need for targeted conservation measures. Additionally, it highlights the importance of sustained, detailed monitoring to mitigate the detrimental effects of climate change on these eels, vital for preserving the Yangtze River's ecological integrity. [ABSTRACT FROM AUTHOR]

Dropout rates are a key indicator of educational quality, making it imperative for educational institutions to design strategies to reduce them, thereby contributing to improved student retention and the achievement of academic objectives. While dropout research has primarily focused on machine learning methods applied to in-person education datasets, this article introduces a novel approach based on time series models for dropout rates analysis at the National Open and Distance University (UNAD). Methodologically, an adaptation of the CRISP-DM methodology was undertaken in four phases, namely: F1. Business and data understanding, F2. Data preparation, F3. Model building and evaluation, and F4. Model deployment. In terms of results, an open dataset on UNAD dropout, obtained from the SPADIES system between 1999 and 2021, was employed. Using Python libraries statsmodels and pandas, an ARIMA model was implemented, displaying optimal error metrics. This ARIMA model was utilized to forecast future dropout rates at UNAD, projecting a future dropout rate fluctuating around 23%. In conclusion, the ARIMA model developed for UNAD stands as an innovative and essential tool in the educational realm, capable of accurately anticipating dropout rates for upcoming semesters. This provides UNAD with a unique advantage in strategic decision-making. [ABSTRACT FROM AUTHOR]

The authors investigate the effects of centering meditation on state hope among college and graduate students through a randomized controlled trial. Participants (n = 150; 65% white, 84% female) were randomized to either a centering meditation group or a waitlist control group. Time‐series analyses indicated that centering meditation significantly improved hope, suggesting long‐term dynamic adjustment, compared to a control group. Specifically, the autoregressive integrated moving average (ARIMA) procedures indicated that the treatment group exhibited a statistically significant upward trend in hope, ARIMA (1, 1, 0). As expected, the control group's levels of hope were stationary, ARIMA (1, 0, 1). The study highlights the potential benefits of centering meditation as an evidence‐based counseling intervention for improving hope in the college population. [ABSTRACT FROM AUTHOR]

This study examines the impact of crude oil and fertiliser price changes on maize crop prices in India using monthly time series data from May 2007 to September 2022. The best‐fitted Autoregressive Integrated Moving Average model with the lowest Akaike's Information Criterion value is selected, and the Box–Ljung test is used to validate the prediction accuracy. Empirical results suggest that maize price is driven by crude oil and fertiliser prices since it has been found that maize prices are highly positively correlated with the prices of crude oil and fertiliser. Also, a strong correlation has been found between crude oil and fertiliser prices. The investigation for forecasting the next 15 months from November 2022 also revealed that maize prices showed no volatility because of a constant trend, but crude oil prices showed a declining trend, while di‐ammonium phosphate prices showed an increasing trend over the period from November 2022 to February 2023. They then declined to June 2023 but again showed an increasing trend from July 2023 to January 2024 and achieved the highest price in December 2023. Because of a constant trend, urea prices showed no volatility over the 15 months. The estimates can aid the government in formulating policies to maintain agricultural crop production and control input price changes to meet the growing population's food demands. [ABSTRACT FROM AUTHOR]

Financial statement analysis is a critical component of decision-making for businesses, investors, and financial professionals. To enhance the accuracy and effectiveness of such analysis, this paper introduces the application of an innovative approach known as the Intelligent Swarm Regression ARIMA Model. This advanced model combines the power of swarm intelligence with ARIMA (AutoRegressive Integrated Moving Average) time series forecasting, offering a robust methodology for predicting and analyzing key financial metrics. The study begins by providing an overview of the Intelligent Swarm Regression ARIMA model and its application to financial data. Through a comprehensive analysis of financial statements, including market capitalization, revenue, net income, and other crucial indicators, the model's efficacy in predicting future values is evaluated. Additionally, the paper examines the deviations between predicted and actual financial values, offering insights into the model's accuracy and areas for potential improvement. The findings of this research are invaluable for investors, financial analysts, and companies seeking to optimize their financial performance and strategic decision-making. By leveraging the Intelligent Swarm Regression ARIMA Model, stakeholders can make well-informed choices that lead to better financial outcomes and a competitive advantage in a dynamic economic landscape. This paper represents a significant step forward in the financial analysis, providing a practical methodology and a pathway to enhanced financial decision-making. As the importance of financial data continues to grow, this research offers a promising avenue for achieving financial success and stability. [ABSTRACT FROM AUTHOR]

The objective of this research article is to analyze the performance of the Autoregressive Integrated Moving Average (ARIMA) model and the 'earlyR' statistical model in predicting epidemic outbreaks using COVID-19 data of India. The results of this analysis can help take preventive actions and restrict the spread of the epidemic with the help of projected data. The 'projections' module was utilized to generate the epidemic path by aligning the available COVID-19 data from India, distribution of the time interval between successive cases and reproduction number (R0) of the corresponding regions with the 'earlyR' epidemic statistical model. The values of (p, d and q) were obtained utilizing the 'auto.arima' function, and an ARIMA time series model was created using the 'forecast' module to forecast future infected occurrences. The 'earlyR' epidemic model yielded a median projected value with an inaccuracy of 35.1 %, while the ARIMA model had a mean error of -1.9 %. A comparison of these methods indicates that the ARIMA model is a superior method compared to the 'earlyR' epidemic model in terms of accuracy. [ABSTRACT FROM AUTHOR]

Background: In 2022, Omicron outbreaks occurred at multiple sites in China. It is of great importance to track the incidence trends and transmission dynamics of coronavirus disease 2019 (COVID-19) to guide further interventions. Methods: Given the population size, economic level and transport level similarities, two groups of outbreaks (Shanghai vs. Chengdu and Sanya vs. Beihai) were selected for analysis. We developed the SEAIQRD, ARIMA, and LSTM models to seek optimal modeling techniques for waves associated with the Omicron variant regarding data predictive performance and mechanism transmission dynamics, respectively. In addition, we quantitatively modeled the impacts of different combinations of more stringent interventions on the course of the epidemic through scenario analyses. Results: The best-performing LSTM model showed better prediction accuracy than the best-performing SEAIQRD and ARIMA models in most cases studied. The SEAIQRD model had an absolute advantage in exploring the transmission dynamics of the outbreaks. Regardless of the time to inflection point or the time to Rt curve below 1.0, Shanghai was later than Chengdu (day 46 vs. day 12/day 54 vs. day 14), and Sanya was later than Beihai (day 16 vs. day 12/day 20 vs. day 16). Regardless of the number of peak cases or the cumulative number of infections, Shanghai was higher than Chengdu (34,350 vs. 188/623,870 vs. 2,181), and Sanya was higher than Beihai (1,105 vs. 203/16,289 vs. 3,184). Scenario analyses suggested that upgrading control level in advance, while increasing the index decline rate and quarantine rate, were of great significance for shortening the time to peak and Rt below 1.0, as well as reducing the number of peak cases and final affected population. Conclusions: The LSTM model has great potential for predicting the prevalence of Omicron outbreaks, whereas the SEAIQRD model is highly effective in revealing their internal transmission mechanisms. We recommended the use of joint interventions to contain the spread of the virus. [ABSTRACT FROM AUTHOR]

Accurate short-term prediction of campus road traffic flow can help improve traffic efficiency, alleviate traffic pressure, and provide travel guidance. This paper proposes a campus road traffic flow short-term prediction method based on an improved extreme learning machine. Firstly, determine the traffic flow parameters of campus roads, including flow rate, occupancy rate, and speed, and use the average of historical traffic flow as the predicted value of future traffic flow. Secondly, the ARIMA model is used to transform non-stationary short-term traffic flow sequences into stationary sequences. Finally, the improved extreme learning machine is used to extract the periodic features of the target road traffic flow data, and the extracted features are input into a random forest for training to obtain short-term traffic flow prediction results. The experimental results show that the proposed method has higher accuracy, shorter prediction time, and higher prediction recall rate. [ABSTRACT FROM AUTHOR]

Objective To analyze the structural changes and influencing factors of total health expenditure in Shandong Province from 2000 to 2020, to provide reference for health department decision -making through short -term prediction. Methods The index data of total health expenditure and influencing factors of total health expenditure in Shandong Province from 2000 to 2020 were selected. The structural change analysis method was used to analyze the changes of health expenditure in Shandong Province, the principal component regression analysis was used to analyze the significant factors affecting the change of health expenditure, and the autoregressive moving average (ARIMA) model was constructed to predict the total health expenditure. Results The structure of health expenditure in Shandong Province changed greatly from 2000 to 2020 (DSV=66.12%). The government health expenditure and social health expenditure showed positive changes, and the structural change values were 0.114 and 0.217, respectively. The personal cash health expenditure showed negative changes, and the structural change value was 一0.331, with obviously optimized health financing structure. The supply of health sendees, demand for health services, total population, aging population, and economic growth showed significant effects on the change of total expenditure, with partial regression coefficients of 0.325, 0.205: 0.101, 0.111, and 0.101, respectively. It was estimated that the total health expenditure in Shandong Province would continue to increase from 2021 to 2025, reaching 718.087 billion yuan by 2025. Conclusion The change of health expenditure is affected by many long-term factors, and financing and cost control should comprehensively consider long-term factors. The change of health expenditure is also affected by some short-term factors, and health development should be vigilant against short-term influencing factors. Trend analysis based on model prediction needs to integrate the potential effects and actual changes of long-term and short-term factors. [ABSTRACT FROM AUTHOR]

Analysis and prediction of water inflow sequence is a research direction for effectively preventing mine inrush water problem. Panlong lead-zinc deposit is of abundant groundwater in a karst area. Because it is adjacent to Qianjiang River section in the Pearl River Basin, with the increasing of mining depth, the prevention and control of tunnel water has become the primary safety issue. Based on the water inflow series from 2010 to 2021, the rescaled range(R/ S) analysis and auto regressive integrated moving average(ARIMA) methods are used to analyze and predict the change trend of water inflow. The water inflow series of Panlong lead-zinc mine is nonlinear and non-stationary, presenting right-sided long tail, peak-flat peak state and non-stationary characteristics. The Hurst index of the annual water inflow series is from 0. 780 1 to 0. 994 9, indicating that the annual change trends of water inflow are similar and seasonal. The Hurst index of interannual water inflow series is 0. 700 5, suggesting that the series has a weaker long-range dependence, compared with the annual series. Based on the water inflow series from 2010 to 2019, the water inflow series of the upper and lower sections of the -120 m elevation in 2020 predicted by ARIMA model, are 4 323. 7 and 6 274. 0 m 3 / h, respectively, the errors are 2. 49% and 3. 14% compared with the measured water inflow. It is suggested that the ARIMA model has the feasibility to effectively predict the change trend of water inflow in a short term and can provide a reference basis for preventing inrush water disasters in Panlong lead-zinc deposit. [ABSTRACT FROM AUTHOR]

Summary: In this paper a semi‐parametric approach is developed to model non‐linear relationships in time series data using polynomial splines. Polynomial splines require very little assumption about the functional form of the underlying relationship, so they are very flexible and can be used to model highly non‐linear relationships. Polynomial splines are also computationally very efficient. The serial correlation in the data is accounted for by modelling the noise as an autoregressive integrated moving average (ARIMA) process, by doing so, the efficiency in nonparametric estimation is improved and correct inferences can be obtained. The explicit structure of the ARIMA model allows the correlation information to be used to improve forecasting performance. An algorithm is developed to automatically select and estimate the polynomial spline model and the ARIMA model through backfitting. This method is applied on a real‐life data set to forecast hourly electricity usage. The non‐linear effect of temperature on hourly electricity usage is allowed to be different at different hours of the day and days of the week. The forecasting performance of the developed method is evaluated in post‐sample forecasting and compared with several well‐accepted models. The results show the performance of the proposed model is comparable with a long short‐term memory deep learning model. [ABSTRACT FROM AUTHOR]

Technical and technological developments in recent decades have stimulated the rapid development of methods and tools in the field of statistical process quality control, which also includes control charts. The principle of control charts defined by Dr. W. Shewhart has been known for more than 100 years. Since then, they have been used in many industries to monitor and control processes. This paper aims to assess the possibilities of use and the selection of the most suitable type of control chart for monitoring the quality of a process depending on its nature. This tool should help operators in monitoring coking time, which is one of the important control variables affecting the quality of coke production. The autoregressive nature of the variable being monitored was considered when selecting a suitable control chart from the group of options considered. In addition to the three traditional types of control charts (Shewhart's, CUSUM, and EWMA), which were applied to the residuals of individual values of different types of ARIMA models, various statistical tests, and plots, a dynamic EWMA control chart was also used. Its advantage over traditional control charts applied to residuals is that it works with directly measured coking time data. This chart is intended to serve as a method to monitor the process. Its role is only to alert the process operator to the occurrence of problems with the length of the coking time. [ABSTRACT FROM AUTHOR]

Ischemic stroke is featured with high incidence, mortality, and disability. The aim of this study is to use Global Burden of Disease database to describe and compare the burden of ischemic stroke in mainland China and Taiwan province and to further predict the expected changes in the next 11 years using statistical modeling methods. Information on ischemic stroke incidence and mortality in China (mainland and Taiwan province) during 1990–2019 was obtained from the Global Burden of Disease database to analyze the effects of region, gender, and age on the incidence and mortality of ischemic stroke in China. The autoregressive integrated moving average model was used to predict the age-standardized incidence rate and age-standardized mortality rate of ischemic stroke in males and females in mainland China and Taiwan province in the next 11 years. The period from 1990 to 2019 witnessed an overall upward trend in the number of incidence and deaths in mainland China and Taiwan province. In 2019, there were nearly 2.87 million ischemic incidence cases with stroke in mainland China, with more female patients than male in the age group of over 60 years. Among the nearly 1.03 million deaths, the death toll of men under the age of 85 years was higher than that of women, while in Taiwan province, the number of incidence was 28 771, with more female patients of all ages than male. Among the 6788 deaths, the death toll of men under the age of 80 years was higher than that of women. In 2019, the age group with the highest number of patients in the two regions was 65–69 years, while the highest number of deaths was found in people aged 85 years and above. As our autoregressive integrated moving average model predicted, the age-standardized incidence rate value of ischemic stroke is expected to be 163.23/100 000 persons in mainland China by 2030, which would continue to increase, while the age-standardized mortality rate value of ischemic stroke is expected to be 16.41/100 000 persons in Taiwan province by 2030, which showed a decreasing trend. Disease burden of ischemic stroke is still increasing in mainland China and Taiwan province, and health resources should be deployed to implement effective prevention and control strategies, taking into account region, gender, and age. [ABSTRACT FROM AUTHOR]

Increasing interest in green energy has popularized the generation of electrical energy, especially from solar energy. Due to the discrete nature of solar energy, future energy production forecasting is very important in terms of uninterrupted and environmentally friendly supply of electrical energy Therefore, it is clear that estimating solar radiation will be vital in planning and managing all processes, from electricity generation, transmission, distribution and consumption. In this study, one-year, six months, four months and two months hourly solar radiation estimation was performed on the hourly basis of four years' solar radiation data obtained four years of hourly data from the station named Afyon Region of the 5th Regional Directorate of Meteorology (MBM), using the Auto Regressive Integrated Moving Average (ARIMA), a timeseries analysis method, ridge regression, and lasso regression estimation models, which are regression models. In addition, the estimation results were improved by applying the Extraterrestrial Radiation Filter (DDIF) in the structures of the methods. The closeness of the relationship between the estimated values obtained by the methods used and the actual radiation values has been tried to be revealed with graphics and statistical methods. [ABSTRACT FROM AUTHOR]

Background: Leishmaniasis is a zoonotic disease and Iran is one of the ten countries with has the highest estimated cases of leishmaniasis. This study aimed to determine the time trend of cutaneous leishmaniasis (CL) incidence using the ARIMA model in Shahroud County, Semnan, Iran. Methods: In this study, 725 patients with leishmaniasis were selected in the Health Centers of Shahroud during 2009–2020. Demographic characteristics including; history of traveling, history of leishmaniasis, co-morbidity of other family members, history of treatment, underlying disease, and diagnostic measures were collected using the patients' information listed in the Health Ministry portal. The Box-Jenkins approach was applied to fit the SARIMA model for CL incidence from 2009 to 2020. All statistical analyses were done by using Minitab software version 14. Results: The mean age of patients was 28.2 ± 21.3 years. The highest and lowest annual incidence of leishmaniasis were in 2018 and 2017, respectively. The average ten-year incidence was 132 per 100,000 population. The highest and lowest incidence of the disease were 592 and 195 for 100,000 population in the years 2011 and 2017, respectively. The best model was SARIMA (3,1,1) (0,1,2)4 (AIC: 324.3, BIC: 317.7 and RMSE: 0.167). Conclusions: This study suggested that time series models would be useful tools for predicting cutaneous leishmaniasis incidence trends; therefore, the SARIMA model could be used in planning public health programs. It will predict the course of the disease in the coming years and run the solutions to reduce the cases of the disease. [ABSTRACT FROM AUTHOR]

Japanese eels (Anguilla japonica) are a typical migratory fish species with high commercial importance. The Pearl River estuary in southern China is an important natural growing ground for Japanese glass eels, but limited information on Japanese glass eel population characteristics is available, despite their ecological importance. In this paper, we examined the annual patterns of the occurrence of Japanese glass eels in the Pearl River estuary from 2011 to 2022. The most frequently occurring Japanese glass eel's total length is 5.3 cm. The collecting period extended from December to February, and the collection catch-per-unit-effort (CPUE) decreased significantly from 2011 to 2022. The generalized linear model (GLM) indicated that daily changes in Japanese glass eel collection were significantly affected by tidal range, water temperature, and lunar distance. The catch peak appeared when the tidal range rose to 1.7 m, and the water temperature dropped below 8 °C on the full moon days. Overall CPUE analysis showed no significant periodic and inter-annual variability in the period 2011–2022, with the ARIMA model suggesting that the CPUE is expected to remain stable but low in the coming years (2023–2026), although recruitment ultimately depends on the overall spawning stock. [ABSTRACT FROM AUTHOR]

Determining the operation scenarios of renewable energies is important for power system dispatching. This paper proposes a renewable scenario generation method based on the hybrid genetic algorithm with variable chromosome length (HGAVCL). The discrete wavelet transform (DWT) is used to divide the original data into linear and fluctuant parts according to the length of time scales. The HGAVCL is designed to optimally divide the linear part into different time sections. Additionally, each time section is described by the autoregressive integrated moving average (ARIMA) model. With the consideration of temporal correlation, the Copula joint probability density function is established to model the fluctuant part. Based on the attained ARIMA model and joint probability density function, a number of data are generated by the Monte Carlo method, and the time autocorrelation, average offset rate, and climbing similarity indexes are established to assess the data quality of generated scenarios. A case study is conducted to verify the effectiveness of the proposed approach. The calculated time autocorrelation, average offset rate, and climbing similarity are 0.0515, 0.0396, and 0.9035, respectively, which shows the superior performance of the proposed approach. [ABSTRACT FROM AUTHOR]

Background: The National Health Security Office (NHSO) has set up the "Rational Drug Use Clinics (RDU)" network to promote the rational use of drugs in community care clinics in Bangkok. Objectives: The objective of the study was to evaluate the effects of educational and financial interventions of antibiotic (ATBs) use in community care clinics in Bangkok. Materials and Methods: A quasi-experimental study, a time series analysis, was conducted. Aggregate weekly prescribing data were extracted from the NHSO Medical Claims Database from October 2013 to September 2019. The Autoregressive Integrated Moving Average (ARIMA) model was utilized to assess intervention effects on the quality of drug use. Five outcomes were measured. Results: ATBs utilization was significantly impacted by the financial intervention (P = 0.004). The percentage of ATBs encounters and drug costs spent on ATBs were impacted by the educational intervention (P = 0.0017, P = 0.001, respectively). The percentage of prescriptions that met the guidelines and the average medicine cost per encounter were not significantly impacted by the interventions. Conclusions: The "RDUs" project was partially effective in improving the quality of drug use in terms of ATB utilization, prescriptions, and costs. The continuation of the qualitative study was supposed to explain why and how interventions affected the quality of drug use. [ABSTRACT FROM AUTHOR]

In order to improve the prediction accuracy of coal mine gas concentration this paper proposes to use autoregressive integrated moving average (ARIMA) model and gated recycler unit (GRU) model to predict each modal component after the variational mode decomposition of gas concentration series. Then a variable weight combination prediction model is constructed by using the induced generalized ordered weighted function average (IGOWFA) operator. The analysis and comparison of experimental data show that compared with the single prediction method or the fixed weight combination prediction method, the prediction results of this method have higher prediction accuracy, which provides a basis for coal mine to judge the probability of gas accidents. [ABSTRACT FROM AUTHOR]

Background This study aimed to characterize the temporal trends of chronic kidney disease (CKD) burden in China during 1990–2019, evaluate their age, period and cohort effects, and predict the disease burden for the next 10 years. Methods Data were obtained from the Global Burden of Disease (GBD) 2019 study. Join-point regression model was used to estimate the average annual percentage change (AAPC) of CKD prevalence and mortality, and the age-period-cohort analysis was used to estimate the age, period and cohort effects. We extended the autoregressive integrated moving average (ARIMA) model to predict the disease burden of CKD in 2020–2029. Results In 2019, there were 150.5 million cases of (10.6%) and 196   726 deaths from (13.8 per 100   000 general population) CKD in China. Between 1990 and 2019, the prevalence and mortality rate of CKD increased significantly from 6.7% to 10.6%, and from 8.3/100   000 to 13.8/100   000. The AAPC was estimated as 1.6% and 1.8%, respectively. Females had a higher CKD prevalence of CKD but a lower mortality rate. Setting the mean level of age, period and cohort as reference groups, the risk of developing CKD increased with age [RRage(15–19) = 0.18 to RRage(85–89) = 2.45]. The cohort risk was significantly higher in the early birth cohort [RRcohort(1905–1909) = 1.56]. In contrast, the increase in age-specific CKD mortality rate after 60–64 years was exponential [RRage(60–64) = 1.24]. The cohort-based mortality risk remained high prior to the 1945–1949 birth cohorts (RRcohort ranging from 1.69 to 1.89) and then declined in the 2000–2004 birth cohort [RRcohort(2000–2004) = 0.22]. The CKD prevalence and mortality are projected to rise to 11.7% and 17.1 per 100   000, respectively, by 2029. Conclusions To reduce the disease burden of CKD, a comprehensive strategy that includes risk factors prevention at the primary care level, CKD screening among the elderly and high-risk population, and access to high-quality medical services is required. [ABSTRACT FROM AUTHOR]

The study predicted chicken meat production in 2019-2025 period for the leading chicken-producing countries with the help of the 1961-2018 Food and Agriculture Organization (FAO) data since chicken meat consumption is so high worldwide. The USA ranks the first place while Brazil and China take second and third places, respectively. The analysis of the pioneer chicken meat-producing countries indicates that while the portion of the USA in world production decreases, the share, particularly Brazil and China, will approach that of the USA. World chicken meat production, which was 7.56 million tons in 1961, will increase to 139.19 million tons in 2025, and this production per capita is predicted to increase to 17.0 kg in 2025 from 2.4, 5.35, 9.80, and 15.0 kg in 1961, 1981, 2001, and 2018, respectively. Indonesia, Russia, Brazil, Japan, and India will run the highest increases in production. However, the share of countries in chicken meat production will decrease from 61% to 60% in 2019-2025 compared to the 2012-2018 periods. This condition showed that apart from some leading countries, the production will keep a rapid increase in production. The increase in chicken meat production and chicken meat import worldwide will improve human nutrition, especially in developing and underdeveloped countries. Countries that run cost advantages and high-quality life standards in line with technological innovations produce processed chicken products, strengthen animal health, hygiene, and transportation standards, and attach importance to advertising activities that increase consumer demand will be more advantageous in this market. [ABSTRACT FROM AUTHOR]

Large-scale forest fires can cause significant ecological losses. Additionally, preserving forest areas may help to slow down climate change. Statistical models are one of the tools used in planning fire management strategies. In this study, the burned forest area of Türkiye is modeled using the Autoregressive Integrated Moving Average (ARIMA) method following the identification, estimation, validation, and forecasting steps. As is known the ARIMA analysis is one of the popular techniques used in time series analysis. Annual total burned forest areas in Türkiye over the period 1940-2021 are considered in the analysis. Three preliminary models are considered for evaluation of their modeling and prediction performances. The models' validities are investigated with Ljung-Box statistics, residual analysis, and cross-validation. According to the results, the ARIMA (3,1,0) model is found to be the most suitable model for predicting the future values of the burned forest area time series in Türkiye. Forecasts for Türkiye's burned forest areas series are obtained for the next 3 years accordingly. [ABSTRACT FROM AUTHOR]

Background: Chronic kidney disease (CKD) is an important global public health issue. In China, CKD affects a large number of patients and causes a huge economic burden. This study provided a new way to predict the number of patients with CKD and estimate its economic burden in China based on the autoregressive integrated moving average (ARIMA) model. Methods: Data of the number of patients with CKD in China from 2000 to 2019 were obtained from the Global Burden of Disease. The ARIMA model was used to fit and predict the number of patients with CKD. The direct and indirect economic burden of CKD were estimated by the bottom-up approach and the human capital approach respectively. Results: The results of coefficient of determination (0.99), mean absolute percentage error (0.26%), mean absolute error (343,193.8) and root mean squared error (628,230.3) showed that the ARIMA (1,1,1) model fitted well. Akaike information criterion (543.13) and Bayesian information criterion (546.69) indicated the ARIMA (1,1,1) model was reliable when analyzing our data. The result of relative error of prediction (0.23%) also suggested that the model predicted well. The number of patients with CKD in 2020 to 2025 was predicted to be about 153 million, 155 million, 157 million, 160 million, 163 million and 165 million respectively, accounting for more than 10% of the Chinese population. The total economic burden of CKD from 2019 to 2025 was estimated to be $179 billion, $182 billion, $185 billion, $188 billion, $191 billion, $194 billion and $198 billion respectively. Conclusion: The number of patients with CKD and the economic burden of CKD will continue to rise in China. The number of patients with CKD in China would increase by 2.6 million (1.6%) per year on average from 2020 to 2025. Meanwhile, the total economic burden of CKD in China would increase by an average of $3.1 billion per year. The ARIMA model is applicable to predict the number of patients with CKD. This study provides a new perspective for more comprehensive understanding of the future risk of CKD. [ABSTRACT FROM AUTHOR]

Background: Chronic kidney disease (CKD) is an important global public health issue. In China, CKD affects a large number of patients and causes a huge economic burden. This study provided a new way to predict the number of patients with CKD and estimate its economic burden in China based on the autoregressive integrated moving average (ARIMA) model. Methods: Data of the number of patients with CKD in China from 2000 to 2019 were obtained from the Global Burden of Disease. The ARIMA model was used to fit and predict the number of patients with CKD. The direct and indirect economic burden of CKD were estimated by the bottom-up approach and the human capital approach respectively. Results: The results of coefficient of determination (0.99), mean absolute percentage error (0.26%), mean absolute error (343,193.8) and root mean squared error (628,230.3) showed that the ARIMA (1,1,1) model fitted well. Akaike information criterion (543.13) and Bayesian information criterion (546.69) indicated the ARIMA (1,1,1) model was reliable when analyzing our data. The result of relative error of prediction (0.23%) also suggested that the model predicted well. The number of patients with CKD in 2020 to 2025 was predicted to be about 153 million, 155 million, 157 million, 160 million, 163 million and 165 million respectively, accounting for more than 10% of the Chinese population. The total economic burden of CKD from 2019 to 2025 was estimated to be $179 billion, $182 billion, $185 billion, $188 billion, $191 billion, $194 billion and $198 billion respectively. Conclusion: The number of patients with CKD and the economic burden of CKD will continue to rise in China. The number of patients with CKD in China would increase by 2.6 million (1.6%) per year on average from 2020 to 2025. Meanwhile, the total economic burden of CKD in China would increase by an average of $3.1 billion per year. The ARIMA model is applicable to predict the number of patients with CKD. This study provides a new perspective for more comprehensive understanding of the future risk of CKD. [ABSTRACT FROM AUTHOR]

Huge collections of published research documents are available in various repositories in Indian universities and research organizations. The efficient retrieval, estimation of research trends, and identification of research gaps with the global trend in different research areas, can be a great guiding tool for regulating research in the appropriate direction. This research attempts to analyse the trend of research activities carried out by Indian researchers in the discipline of Computer Science from 2010 to 19 and forecast the upcoming research trend for the years 2020–23. A repository of the abstracts based on domains given in the Computer Science Ontology (CSO) published by the Indian researchers is developed from the Scopus database. Document Index Graph document representation model is used to store the repository, shared phrases across the documents are extracted and phrase‐based similarity is computed. Combining the Single‐term and Phrase‐based similarity, the hybrid similarity is generated and similar documents are clustered using the DBSCAN clustering technique. Topics are identified for each cluster using the Latent Dirichlet Allocation algorithm and are automatically labelled using CSO. For each topic, the trend analysis and forecasting have been done using the Auto‐Regressive Integrated Moving Average. For the assessment of the forecasting performance, the dataset from 2010 to 17 is used as a training dataset and 2018–19 as a testing dataset. The average forecasting for the year 2018 for all CSO domains belongs to the Good forecasting category with Mean Absolute Percentage Error (MAPE) 18.34, and 2019 shows reasonable forecasting with MAPE 30.20 as per the MAPE interpretation given by Lewis. For each topic the average forecasting for years 2018–19 shows either Highly accurate, Good or Reasonable forecasting. As a result, the top four domains for the years 2020–23 are also identified which can help initial researchers in the identification of a relevant topic for research and exploration. [ABSTRACT FROM AUTHOR]

Nowadays, air quality prediction is the most essential process taken by an Indian government. Due to poor quality of air, unhealthy lifestyle and premature deaths of humans have arisen in India, especially in Delhi. Not only has a human's health, but the air pollution also made a huge impact on several areas like economy, agriculture and road accidents, etc. In recent times, deep learning (DL) technologies are influenced every application rapidly even in air pollution prediction. In this work, the novel optimised DL algorithms are proposed for the efficient prediction of air quality particularly focussing on Chennai, Tamil Nadu. To provide higher accuracy in air quality prediction, the novel optimised DL algorithms are proposed which is combined several models like ARIMA and CNN-LSTM and Tuna Optimization Algorithm, respectively. Initially, CNN and LSTM are combined to provide hybrid architecture. Next, the metaheuristics-based tuna swarm optimization model is applied for fine-tuning the hyperparameters of the CNN-LSTM model which is known as the Tuna Optimised CNN-LSTM (TOCL) method. Finally, the novel TOCL is applied to the residuals of the ARIMA model to form an ARIMA- TOCL (ARTOCL) model. As a result, the novel ARTOCL is learned and performed with an optimal air quality prediction. The metrics of the Hybrid ARTOCL model are evaluated as a better mean absolute error (MAE), root mean squared error (RMSE), R2 score and the normalized RMSE (nRMSE) with higher accuracy than the previous models. The results show that the proposed prediction model has 22.6% R2 improvement, 14.6% MAE reductions, 22% RMSE reductions and 16.45% nRMSE reductions than the existing models. [ABSTRACT FROM AUTHOR]

In this paper, based on the discrete lifetime distribution, the residual and past of the Tsallis and Renyi extropy are introduced as new measures of information. Moreover, some of their properties and their relation to other measures are discussed. Furthermore, an example of a uniform distribution of the obtained models is given. Moreover, the softmax function can be used as a discrete probability distribution function with a unity sum. Thus, applying those measures to the softmax function for simulated and real data is demonstrated. Besides, for real data, the softmax data are fit to a convenient ARIMA model. [ABSTRACT FROM AUTHOR]

• Ecological protection scenario slightly outperforms inertia development scenario. • A Positive correlation between carbon emissions and the comprehensive degree index. • Carbon emissions' key factor is construction land carbon emissions predicted by ARIMA. • Substituting coal with clean energy is essential for low-carbon emission objectives. Research on future land cover changes and carbon emissions is essential for effective land resource management and developing feasible carbon mitigation strategies. This study focused on the Yellow River Basin and employed the Future Land Use Simulation (FLUS) and Autoregressive Integrated Moving Average (ARIMA) models to project future land cover and carbon emissions. Additionally, bivariate spatial autocorrelation was utilized to analyze the relationship between them. Key findings are as follows: 1) Historically, the Yellow River Basin has experienced an expansion in construction land, forests, grasslands, and water, while cropland and unused land have diminished. Notably, construction land displayed the most significant changes, whereas grasslands showed minimal modification. Looking ahead, both the ecological protection and inertial development scenarios exhibit consistent trends with historical patterns across the land type categories. In contrast, the economic priority development scenario forecasts an increase in construction land, cropland, and grasslands, indicating a distinct shift compared to the other scenarios. However, the ecological protection scenario proves to be more sustainable. 2) In the absence of intervention, the simulated carbon emissions from construction land throughout the basin display a linear increase across various scenarios, with provincial-level variations showing an increase from southwest to northeast. However, Henan and Sichuan are expected to experience slower reductions in carbon emissions, compared to other projections. There is a notable positive correlation between carbon emissions and the comprehensive index, indicating that regions with high emissions typically experience substantial land and economic development. 3) Energy consumption projections for 2030 and 2060 indicate that to align with China's carbon goals, it is essential to reduce energy consumption and adjust the fossil to non-fossil fuel ratio to reduce carbon emissions. Substituting coal with clean energy and enhancing energy efficiency will be more effective for achieving low-carbon emission targets. In summary, this study provides significant guidance for China's ecological conservation, low-carbon emission strategies, and global carbon emission control efforts. [ABSTRACT FROM AUTHOR]

[Display omitted] • The carrying capacity of water resources in Gansu Province is evaluated comprehensively. • The combination weights of different indicators were determined through the application of game theory. • The TOPSIS evaluation model improved by using grey relational analysis was applied to calculate the WRCC of Gansu Province. • The water-carrying capacity of 14 cities and prefectures in Gansu Province has been progressively increasing over the years. • The future water resource carrying capacity has been forecasted using the ARIMA model. The water resource carrying capacity, as a pivotal component of the overall regional natural resource carrying capacity, plays a fundamental role in determining the feasibility of achieving harmonious coexistence among population, economy, and environment in regions with limited water resources. It holds paramount significance for regional development."Based on the theoretical framework of the four subsystems, namely water resources, society, economy, and ecology, this article employs the entropy weight method and CRITIC method to calculate individual weights. The combined weights are then determined using game theory. Subsequently, an improved TOPSIS model based on grey relational analysis is applied to comprehensively evaluate the water resources carrying capacity in Gansu Province from 2009 to 2022. The main obstacles impacting water resources carrying capacity are diagnosed using an obstacle degree model. Finally, a time series model is utilized to predict future trends in water resources carrying capacity from 2023 to 2030. The findings suggest that (1)over the study period, Gansu Province exhibited an upward trend in its overall water resource carrying capacity, with a value of 0.55 (weakly bearable) in 2022, representing a significant 20% increase compared to the baseline year of 2009. (2) In terms of regional changes, before 2015, the water resources carrying capacity of Wuwei, Linxia, Dingxi, and other places were in a weakly bearable carrying range [ 0.5,0.6), while other cities and prefectures were in a critical carrying state [ 0.4,0.5). After 2015, the carrying state of most cities and prefectures has improved, especially in Jiuquan, Jiayuguan, Wuwei, and other places, which may be related to the local government's efforts to increase water resources management and water-saving measures. (3) The key factors influencing the carrying capacity of water resources in Gansu Province include ecological water consumption, industrial wastewater emissions of chemical oxygen demand (COD), urban sewage treatment capacity, effective irrigation area in agricultural land, overall water consumption rate, and per capita daily water consumption in urban areas. To promote sustainable utilization of water resources, it is crucial to strengthen the management and regulation of these major hindering factors. (4) The future carrying capacity of water resources in various cities and states is expected to witness significant improvement. By 2030, it is anticipated that the carrying capacity of all provinces within Gansu Province will reach a bearable state [0.6,1], thereby progressing towards an overall green and sustainable trajectory. [ABSTRACT FROM AUTHOR]

Objective: To analyze the prevalence trend of Sjögren's syndrome in the Department of Immunology and Rheumatology of Nanjing Zhongda Hospital from January 2015 to December 2019, and compare the application of SARIMA model and Holt‐Winters model in predicting the number of cases of Sjögren's syndrome. Methods: All of the data from the Department of Immunology and Rheumatology of Nanjing Zhongda Hospital were collected. The number of monthly cases from January 2015 to December 2019 was regarded as the training set, and it was used to establish the SARIMA model and Holt‐Winters model. The number of monthly incidences from January 2020 to December 2020 was regarded as the test set, and it was used to check the model performance. Results: The optimal model of SARIMA is ARIMA (0,1,1) (2,1,1)12 model, and the optimal model of Holt‐Winters model is Holt‐Winters addition model. It was found that the Holt‐Winters addition model produced the smallest error. Conclusion: Holt‐Winters addition model produces better prediction accuracy of the model. [ABSTRACT FROM AUTHOR]

In order to promote the economic development of China's provinces and provide references for the provinces to make effective economic decisions, it is urgent to investigate the trend of province-level economic development. In this study, DMSP/OLS data and NPP/VIIRS data were used to predict economic development. Based on the GDP data of China's provinces from 1992 to 2016 and the nighttime light remote sensing (NTL) data of corresponding years, we forecast GDP via the linear model (LR model), ARIMA model, ARIMAX model, and SARIMA model. Models were verified against the GDP records from 2017 to 2019. The experimental results showed that the involvement of NTL as exogenous variables led to improved GDP prediction. [ABSTRACT FROM AUTHOR]