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Olea europaea L. is an emblematic tree plantation of the Mediterranean basin and one of the main sources of allergenic pollen. In this study, we examined variations in airborne Olea pollen season, trends and built forecast models based on multiple regression analysis over a 13-year period (2008–2019, 2022) in NW of Morocco (Tétouan), focusing on start date of pollination (SDP), end date of pollination (EDP), peak date (PD), and pre-peak pollen Integral (PPI). Spearman's correlation analysis highlighted the importance of different pre-season meteorological parameters on the features of Olea pollen season depending on the period considered. SDP became earlier with increasing minimum temperature in March, while EDP was mainly influenced by precipitation in February and PD is earlier with increasing maximum temperature and precipitation in February. Linear regression results indicated a trend toward a shorter pollination period, almost significant, by delaying SDP rather than earlier EDP, probably due to the significant decrease in minimum temperature between January and April. The best regression models predicted the characteristics of the Olea pollen season to within 2 days and a value close to the PPI at 45 pollen*day/m3, and achieved an accuracy between 58 and 95%. The strongest predictors when forecasting SDP, EDP, PD and PPI were minimum temperature in March, precipitation in April, maximum temperature in February and minimum temperature in November, respectively. Findings suggest that olive reproductive cycle is considerably dependent on pre-season meteorological parameters. Further performed statistical analysis should be made to improve traditional models using a long data series. [ABSTRACT FROM AUTHOR]

Considering sustainability and resource efficiency goals, the circular economy has gained significant attention as a promising economic model. For the country's sustainable development, policymakers need to design a new economic framework that targets the reduction of waste generation and the enhancement of its usage. To achieve this, countries worldwide focus on total municipal waste generation and its disposal methods with a focus on recycling. Considering this need, the Organisation for Economic Co-operation and Development (OECD) explored twelve factors associated with the Circular Economy (CE) for European countries. For a better understanding, a correlation analysis was conducted on total municipal waste generation and recycling for eleven countries. Trend analysis is also performed to understand how countries should plan their future policy designs while keeping sustainability in the limelight. The study employs an autoregressive integrated moving average (ARIMA) model to predict trends in two crucial components of the CE: municipal waste generation and recycling rates. The predicting modelling approach will provide a targeted measure for policymakers to handle waste efficiently. [ABSTRACT FROM AUTHOR]

This study aims to analyse housing prices in Taiwan in the post-epidemic era, identify the crucial factors influencing them, and develop a suitable method for analysing and forecasting them. This study collects relevant data such as Taiwan's housing price index data from 2002 to 2020 to identify the crucial factors affecting Taiwan's housing prices; this is achieved by constructing a regression model, forecasting Taiwan's housing prices through a constructed long short-term memory (LSTM) model that employs big data analytics, and verifying the efficiency of the proposed models through R-square and root mean square error values. The results indicate that the top 10 factors affecting Taiwan's housing prices are mostly related to mortgage interest rates, suggesting that in Taiwan, the effect on housing prices in the post-epidemic era may be non-significant. This study collects data on Taiwan's housing price for the period from the first quarter of 2002 to the fourth quarter of 2020 to construct an LSTM for forecasting Taiwan's housing prices. The results indicate that the proposed LSTM exhibits good fitness, indicating that the model is suitable for analysing and forecasting housing prices. Given that analysing and forecasting quantity is also crucial in housing market analyses and that this study focuses only on predicting housing prices, future research should explore the simultaneous prediction and analysis of both price and quantity. [ABSTRACT FROM AUTHOR]

Mental health has been identified by the World Health Organization (WHO) as a matter of concern internationally. In Colombia, specifically, the importance of mental health care, including the phenomenon of suicide, has been highlighted. In this regard, policymakers have sought alternatives to address or mitigate this issue. In this context, research becomes relevant regarding the development of tools that facilitate decision-making for governmental authorities, for example, through the formulation of forecasting models that enable the identification of trends and patterns of behaviour of suicide attempts. The objective of this study is to formulate and select a regional forecasting model for suicide attempts by means of predictive analytics techniques. This paper contributes to fill the gap in the case of suicide attempts in the city of Medellín, Colombia. The fitting, validation and comparison of three different time series models, under minimum forecast error criteria is presented. The compared models include parametric, Holt-Winters and Box-Jenkins approximations; For the analysed data, the parametric model with cubic, seasonal and ARMA(0.5) components is the one with the lowest forecast error. This model manages to capture the trends of the phenomenon, with a low level of error for the projection for nearby trends, but it does not manage to respond to sudden changes in structure such as those that occurred in the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

The global concentration of fine particulate matter (PM2.5) is experiencing an upward trend. This study investigates the utilization of space-time cubes to visualize and interpret PM2.5 data in South Africa over multiple temporal intervals spanning from 1998 to 2022. The findings indicated that the mean PM2.5 concentrations in Gauteng Province were the highest, with a value of 53 μg/m3 in 2010, whereas the lowest mean PM2.5 concentrations were seen in the Western Cape Province, with a value of 6.59 μg/m3 in 1999. In 2010, there was a rise in the average concentration of PM2.5 across all provinces. The increase might be attributed to South Africa being the host nation for the 2010 FIFA World Cup. In most provinces, there has been a general trend of decreasing PM2.5 concentrations over the previous decade. Nevertheless, the issue of PM2.5 remains a large reason for apprehension. The study also forecasts South Africa's PM2.5 levels until 2029 using simple curve fitting, exponential smoothing and forest-based models. Spatial analysis revealed that different areas require distinct models for accurate forecasts. The complexity of PM2.5 trends underscores the necessity for varied models and evaluation tools.

In today's dynamic and competitive manufacturing landscape, accurate demand forecasting is paramount for optimizing production processes, reducing inventory costs, and meeting customer demands efficiently. With the advent of Artificial Intelligence (AI), there has been a significant evolution in demand forecasting methods, enabling manufacturers to enhance the accuracy of the forecasts. This systematic literature review aims to provide a comprehensive overview of the state-of-the-art on demand forecasting models in the manufacturing sector, whether AI-based models or hybrid methods merging both the AI technology and classical demand forecasting methods. The review begins by establishing an overview on demand forecasting methods, it then outlines the systematic methodology used for the literature search. The review encompasses a wide range of scholarly articles published up to September 2023. A rigorous screening process is applied to select relevant studies. Accordingly, a thorough analysis in the basis of the forecasting methods adopted and data used have been carried out. By synthesizing the existing knowledge, this review contributes to the ongoing advancement of demand forecasting practices in the manufacturing sector providing researchers and practitioners an overview on the advancements on the use of AI models to improve the accuracy of demand forecasting models.

Background: The purpose was to analyse the interrelations between planning and complexity metrics and gamma passing rates (GPRs) obtained from VMAT treatments and build the forecasting models for qualitative prediction (QD) of GPRs results. Materials and method: 802 treatment arcs from the plans prepared for the head and neck, thorax, abdomen, and pelvic cancers were analysed. The plans were verified by portal dosimetry and analysed twice using the gamma method with 3%|2mm and 2%|2mm acceptance criteria. The tolerance limit of GPR was 95%. Red, yellow, and green QDs were established for GPR examination. The interrelations were examined, as well as the analysis of effective differentiation of QD. Three models for QD forecasting based on discriminant analysis (DA), random decision forest (RDF) methods, and the hybrid model (HM) were built and evaluated. Results: Most of the interrelations were small or moderate. The exception is correlations of the join function with the average number of monitor units per control point (R = 0.893) and the beam aperture with planning target volume (R = 0.897). While many metrics allow for the effective separation of the QDs from each other, the study shows that predicting the values of the QD is possible only through multi-component forecasting models, of which the HM is the most accurate (0.894). Conclusion: Of the three models explored in this study, the HM, which uses DA methods to predict red QD and RDF methods to predict green and yellow QDs, is the most promising one. [ABSTRACT FROM AUTHOR]

This review deals with major diseases caused by fungi and oomycetes in the citrus supply chain, including post-harvest fruit diseases, and summarizes the strategies and techniques that may be adopted to prevent the damages and losses they cause. Its scope is to highlight the contribute that smart technologies provide towards new solutions for sustainable and safe management strategies of these diseases. Particular attention is given to the application of biopesticides, natural substances, resistance inducers and biostimulants to prevent fruit rots. The review focuses also on mycotoxins and mycotoxigenic fungi that contaminate fresh fruit and food products derived from citrus fruit, an aspect that has been little investigated and regulated so far. An additional relevant aspect addressed by the review is the early detection and routine diagnosis of fungal and oomycete pathogens that threat the international trade and long-distance shipment of citrus fruit, with a particular emphasis on quarantine pathogens. In this respect, the opportunities offered by new practical, rapid, sensitive and robust molecular diagnostic methods are briefly discussed. [ABSTRACT FROM AUTHOR]

This study investigates the forecasting of cardiovascular mortality trends in Greece’s elderly population. Utilizing mortality data from 2001 to 2020, we employ two forecasting models: the Autoregressive Integrated Moving Average (ARIMA) and Facebook's Prophet model. Our study evaluates the efficacy of these models in predicting cardiovascular mortality trends over 2020-2030. The ARIMA model showcased predictive accuracy for the general and male population within the 65-79 age group, whereas the Prophet model provided better forecasts for females in the same age bracket. Our findings emphasize the need for adaptive forecasting tools that accommodate demographic-specific characteristics and highlight the role of advanced statistical methods in health policy planning. [ABSTRACT FROM AUTHOR]

The global concentration of fine particulate matter (PM2.5) is experiencing an upward trend. This study investigates the utilization of space-time cubes to visualize and interpret PM2.5 data in South Africa over multiple temporal intervals spanning from 1998 to 2022. The findings indicated that the mean PM2.5 concentrations in Gauteng Province were the highest, with a value of 53 μg/m3 in 2010, whereas the lowest mean PM2.5 concentrations were seen in the Western Cape Province, with a value of 6.59 μg/m3 in 1999. In 2010, there was a rise in the average concentration of PM2.5 across all provinces. The increase might be attributed to South Africa being the host nation for the 2010 FIFA World Cup. In most provinces, there has been a general trend of decreasing PM2.5 concentrations over the previous decade. Nevertheless, the issue of PM2.5 remains a large reason for apprehension. The study also forecasts South Africa's PM2.5 levels until 2029 using simple curve fitting, exponential smoothing and forest-based models. Spatial analysis revealed that different areas require distinct models for accurate forecasts. The complexity of PM2.5 trends underscores the necessity for varied models and evaluation tools. [ABSTRACT FROM AUTHOR]

Poverty is widely acknowledged as a significant factor in the outbreak of armed conflicts, particularly fueling armed conflict within national borders. There is a compelling argument positing that poverty is a primary catalyst for intrastate armed conflicts; reciprocally, these conflicts exacerbate poverty. This article introduces a statistical model to forecast the likelihood of armed conflict within a country by scrutinizing the intricate relationship between intrastate armed conflicts and various facets of poverty. Poverty, arising from factors such as gender inequality and limited access to education and public services, profoundly affects social cohesion. Armed conflicts, a significant cause of poverty, result in migration, economic devastation, and adverse effects on social unity, particularly affecting disadvantaged and marginal groups. Forecasting and receiving early warnings for intrastate armed conflicts are crucial for international policymakers to take precautionary measures. Anticipating and proactively addressing potential conflicts can mitigate adverse consequences and prevent escalation. Hence, forecasting intrastate armed conflicts is vital, prompting policymakers to prioritize the development of effective strategies to mitigate their impact. While not guaranteeing absolute certainty in forecasting future armed conflicts, the model shows a high degree of accuracy in assessing security risks related to intrastate conflicts. It utilizes a machine‐learning algorithm and annually published fragility data to forecast future intrastate armed conflicts. Despite the widespread use of machine‐learning algorithms in engineering, their application in social sciences still needs to be improved. This article introduces an innovative approach to examining the correlation between various dimensions of poverty and armed conflict using machine‐learning algorithms.

Accurate forecasting of heat demand in district heating networks is essential for their efficient and sustainable operation. This paper presents a novel approach using a random parameter linear regression model to forecast heat demand, distinguishing itself from classical linear regression models by its ability to address unobserved heterogeneity among parameters. Through a case study in Estonia and utilizing data from 2018 to 2023 and considering seasonality and consumption patterns, the study investigates determinants of heating demand in district heating networks. Two models were trained for heating and non-heating seasons. Results indicate significant impacts of weather conditions, energy prices, time of day, and network infrastructure on heat supply during the heating season, while only time of day and electricity prices were significant drivers during the non-heating season, with no notable influence of weather conditions. Prediction accuracy was slightly enhanced using the random parameter linear regression model, with a mean absolute percentage error of 9.66 % compared to 9.99 % for the Multi Linear Regression Model on the testing set.

Myoelectric indices forecasting is important for muscle fatigue monitoring in wearable technologies, adaptive control of assistive devices like exoskeletons and prostheses, functional electrical stimulation (FES)-based Neuroprostheses, and more. Non-stationary temporal development of these indices in dynamic contractions makes forecasting difficult. This study aims at incorporating transfer learning into a deep learning model, Myoelectric Fatigue Forecasting Network (MEFFNet), to forecast myoelectric indices of fatigue (both time and frequency domain) obtained during voluntary and FES-induced dynamic contractions in healthy and post-stroke subjects respectively. Different state-of-the-art deep learning models along with the novel MEFFNet architecture were tested on myoelectric indices of fatigue obtained during ${a}\text {)}$ voluntary elbow flexion and extension with four different weights (1 kg, 2 kg, 3 kg, and 4 kg) in sixteen healthy subjects, and ${b}\text {)}$ FES-induced elbow flexion in sixteen healthy and seventeen post-stroke subjects under three different stimulation patterns (customized rectangular, trapezoidal, and muscle synergy-based). A version of MEFFNet, named as pretrained MEFFNet, was trained on a dataset of sixty thousand synthetic time series to transfer its learning on real time series of myoelectric indices of fatigue. The pretrained MEFFNet could forecast up to 22.62 seconds, 60 timesteps, in future with a mean absolute percentage error of 15.99 ± 6.48% in voluntary and 11.93 ± 4.77% in FES-induced contractions, outperforming the MEFFNet and other models under consideration. The results suggest combining the proposed model with wearable technology, prosthetics, robotics, stimulation devices, etc. to improve performance. Transfer learning in time series forecasting has potential to improve wearable sensor predictions.

The incorporation of renewable energy systems in the world energy system has been steadily increasing during the last few years. In terms of the building sector, the usual consumers are becoming increasingly prosumers, and the trend is that communities of energy, whose households share produced electricity, will increase in number in the future. Another observed tendency is that the aggregator (the entity that manages the community) trades the net community energy in public energy markets. To accomplish economically good transactions, accurate and reliable forecasts of the day-ahead net energy community must be available. These can be obtained using an ensemble of multi-step shallow artificial neural networks, with prediction intervals obtained by the covariance algorithm. Using real data obtained from a small energy community of four houses located in the southern region of Portugal, one can verify that the deterministic and probabilistic performance of the proposed approach is at least similar, typically better than using complex, deep models. [ABSTRACT FROM AUTHOR]

Can we predict fine wine and alcohol prices? Yes, but it depends on the forecasting horizon. We make this point by considering the Liv-ex Fine Wine 100 and 50 Indices, the retail and wholesale alcohol prices in the United States for the period going from January 1992 to March 2022. We use rich and diverse datasets of economic, survey, and financial variables as potential price drivers and adopt several combination/dimension reduction techniques to extract the most relevant determinants. We build a comprehensive set of models and compare forecast performances across different selling levels and alcohol categories. We show that it is possible to predict fine wine prices for the 2-year horizon and retail/wholesale alcohol prices at horizons ranging from 1 month to 2 years. Our findings stress the importance of including consumer survey data and macroeconomic factors, such as international economic factors and developed markets equity risk factors, to enhance the precision of predictions of retail/wholesale (fine wine) prices. [ABSTRACT FROM AUTHOR]

Anthropic fires are hugely responsible for the deforestation of the Brazilian legal Amazon region, one of the world's most important ecosystems. In this way, policies must be implemented to reduce such a phenomenon. Preventive policies must be manifold, but historical time series analysis and active fire behavior predictions might subside part of the actions. Following this track, the present study aims to understand the spatiotemporal dynamics of active fire, using historical data of active fire comprising all states in the Brazilian legal Amazon region from January 2000 to December 2022. We used information from the National Institute for Space Research to fit appropriate statistical models for counting time series and forecast the monthly behavior of active fire until May 2023. The results showed that approximately 73% of active fires were registered in Pará, Mato Grosso, and Rondônia states, mainly between 2000 and 2010. In Pará, the forecast obtained reflects the phenomenon's seasonality, with a higher concentration between August and November. In summary, the adopted methodology provided results that facilitated an understanding of the dynamics of active fire in the Brazilian legal Amazon region and can be considered a relevant tool to help authorities formulate public policies for arson prevention. [ABSTRACT FROM AUTHOR]

Abstract Background The COVID-19 pandemic has disrupted the healthcare system, leading to delays in detection of other non-COVID-19 diseases. This paper presents ANE Framework (Analytics for Non-COVID-19 Events), a reliable and user-friendly analytical forecasting framework designed to predict the number of patients with non-COVID-19 diseases. Prior to 2020, there were analytical models focused on specific illnesses and contexts. Then, most models have focused on understanding COVID-19 behavior. There is a lack of analytical frameworks that enable disease forecasting for non-COVID-19 diseases. Methods The ANE Framework utilizes time series analysis to generate forecasting models. The framework leverages daily data from official government sources and employs SARIMA models to forecast the number of non-COVID-19 cases, such as tuberculosis and suicide attempts. Results The framework was tested on five different non-COVID-19 events. The framework performs well across all events, including tuberculosis and suicide attempts, with a Mean Absolute Percentage Error (MAPE) of up to 20% and the consistency remains independent of the behavior of each event. Moreover, a pairwise comparison of averages can lead to over or underestimation of the impact. The disruption caused by the pandemic resulted in a 17% gap (2383 cases) between expected and reported tuberculosis cases, and a 19% gap (2464 cases) for suicide attempts. These gaps varied between 20 and 64% across different cities and regions. The ANE Framework has proven to be reliable for analyzing several diseases and exhibits the flexibility to incorporate new data from various sources. Regular updates and the inclusion of new associated data enhance the framework's effectiveness. Conclusions Current pandemic shows the necessity of developing flexible models to be adapted to different illness data. The framework developed proved to be reliable for the different diseases analyzed, presenting enough flexibility to update with new data or even include new data from different databases. To keep updated on the result of the project allows the inclusion of new data associated with it. Similarly, the proposed strategy in the ANE framework allows for improving the quality of the obtained results with news events.

Background: The COVID-19 pandemic has disrupted the healthcare system, leading to delays in detection of other non-COVID-19 diseases. This paper presents ANE Framework (Analytics for Non-COVID-19 Events), a reliable and user-friendly analytical forecasting framework designed to predict the number of patients with non-COVID-19 diseases. Prior to 2020, there were analytical models focused on specific illnesses and contexts. Then, most models have focused on understanding COVID-19 behavior. There is a lack of analytical frameworks that enable disease forecasting for non-COVID-19 diseases. Methods: The ANE Framework utilizes time series analysis to generate forecasting models. The framework leverages daily data from official government sources and employs SARIMA models to forecast the number of non-COVID-19 cases, such as tuberculosis and suicide attempts. Results: The framework was tested on five different non-COVID-19 events. The framework performs well across all events, including tuberculosis and suicide attempts, with a Mean Absolute Percentage Error (MAPE) of up to 20% and the consistency remains independent of the behavior of each event. Moreover, a pairwise comparison of averages can lead to over or underestimation of the impact. The disruption caused by the pandemic resulted in a 17% gap (2383 cases) between expected and reported tuberculosis cases, and a 19% gap (2464 cases) for suicide attempts. These gaps varied between 20 and 64% across different cities and regions. The ANE Framework has proven to be reliable for analyzing several diseases and exhibits the flexibility to incorporate new data from various sources. Regular updates and the inclusion of new associated data enhance the framework's effectiveness. Conclusions: Current pandemic shows the necessity of developing flexible models to be adapted to different illness data. The framework developed proved to be reliable for the different diseases analyzed, presenting enough flexibility to update with new data or even include new data from different databases. To keep updated on the result of the project allows the inclusion of new data associated with it. Similarly, the proposed strategy in the ANE framework allows for improving the quality of the obtained results with news events. [ABSTRACT FROM AUTHOR]

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The Brazilian industrial sector is the largest electricity consumer in the power system. Energy planning in this sector is important mainly due to its economic, social, and environmental impact. In this context, electricity consumption analysis and projections are highly relevant for the decision-making of the industrial sectorand organizations operating in the energy system. The electricity consumption data from the Brazilian industrial sector can be organized into a hierarchical structure composed of each geographic region (South, Southeast, Midwest, Northeast, and North) and their respective states. This work proposes a hybrid approach that incorporates the projections obtained by the exponential smoothing and Box–Jenkins models to obtain the hierarchical forecasting of electricity consumption in the Brazilian industrial sector. The proposed approach was compared with the bottom-up, top-down, and optimal combination approaches, which are widely used for time series hierarchical forecasting. The performance of the models was evaluated using the mean absolute percentage error (MAPE) and root mean squared error (RMSE) precision measures. The results indicate that the proposed hybrid approach can contribute to the projection and analysis of industrial sector electricity consumption in Brazil.

The aim of this study is to develop the inventory planning system of a Portuguese tyre retailer based on forecasting sales models. Using sales history up to 2020, tyres were grouped into three levels of sales aggregation and different quantitative forecasting models were applied. The comparison of these models resorted to various evaluation measures to choose the most suitable one for each group. The study shows that for items with sales grouped monthly and for items with sales grouped by semester, Holt's method had a better performance on determining sales forecasts, while for tyres with sales grouped quarterly, it was Croston's method that stood out. The inventory policy outlined for each group of items reflects the results of the forecasted demand, and the review period depends on the sales group under analysis. In agreement with previous studies, the usefulness of statistical methods is corroborated. Additionally, the advantage of combining the said methods proved helpful, particularly as a starting point for tyre retail inventory planning. [ABSTRACT FROM AUTHOR]

This work proposes a procedure for the multi-objective design of a robust forecasting ensemble of data-driven models. Starting with a data-selection algorithm, a multi-objective genetic algorithm is then executed, performing topology and feature selection, as well as parameter estimation. From the set of non-dominated or preferential models, a smaller sub-set is chosen to form the ensemble. Prediction intervals for the ensemble are obtained using the covariance method. This procedure is illustrated in the design of four different models, required for energy management systems. Excellent results were obtained by this methodology, superseding the existing alternatives. Further research will incorporate a robustness criterion in MOGA, and will incorporate the prediction intervals in predictive control techniques. [ABSTRACT FROM AUTHOR]

The creation of national energy strategy cannot proceed without accurate projections of future electricity consumption; this is because EC is intimately tied to other forms of energy, such as oil and natural gas. For the purpose of determining and bettering overall energy consumption, there is an urgent requirement for accurate monitoring and calculation of EC at the building level using cutting-edge technology such as data analytics and the internet of things (IoT). Soft computing is a subset of AI that tries to design procedures that are more accurate and reliable, and it has proven to be an effective tool for solving a number of issues that are associated with the use of energy. The use of soft computing for energy prediction is an essential part of the solution to these kinds of challenges. This study presents an improved version of the Harris Hawks Optimization model by combining it with the IHHODL-ECP algorithm for use in Internet of Things settings. The IHHODL-ECP model that has been supplied acts as a useful instrument for the prediction of integrated energy consumption. In order for the raw electrical data to be compatible with the subsequent processing in the IHHODL-ECP model, it is necessary to perform a preprocessing step. The technique of prediction uses a combination of three different kinds of deep learning models, namely DNN, GRU, and DBN. In addition to this, the IHHO algorithm is used as a technique for making adjustments to the hyperparameters. The experimental result analysis of the IHHODL-ECP model is carried out under a variety of different aspects, and the comparison inquiry highlighted the advantages of the IHHODL-ECP model over other present approaches. According to the findings of the experiments conducted with an hourly time resolution, the IHHODL-ECP model obtained a MAPE value of 33.85, which was lower than those produced by the LR, LSTM, and CNN-LSTM models, which had MAPE values of 83.22, 44.57, and 34.62 respectively. These findings provided evidence of the IHHODL-ECP model's improved ability to provide accurate forecasts. [ABSTRACT FROM AUTHOR]

Wind is a dominant source of renewable energy with a high sustainability potential. However, the intermittence and unstable nature of wind source affect the efficiency and reliability of wind energy conversion systems. The prediction of the available wind potential is also heavily flawed by its unstable nature. Thus, evaluating the wind energy trough wind speed prevision, is crucial for adapting energy production to load shifting and user demand rates. This work aims to forecast the wind speed using the statistical Seasonal Auto-Regressive Integrated Moving Average (SARIMA) model and the Deep Neural Network model of Long Short-Term Memory (LSTM). In order to shed light on these methods, a comparative analysis is conducted to select the most appropriate model for wind speed prediction. The errors metrics, mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are used to evaluate the effectiveness of each model and are used to select the best prediction model. Overall, the obtained results showed that LSTM model, compared to SARIMA, has shown leading performance with an average of absolute percentage error (MAPE) of 14.05%.

This paper presents a review of the literature on network traffic prediction, while also serving as a tutorial to the topic. We examine works based on autoregressive moving average models, like ARMA, ARIMA and SARIMA, as well as works based on Artifical Neural Networks approaches, such as RNN, LSTM, GRU, and CNN. In all cases, we provide a complete and self-contained presentation of the mathematical foundations of each technique, which allows the reader to get a full understanding of the operation of the different proposed methods. Further, we perform numerical experiments based on real data sets, which allows comparing the various approaches directly in terms of fitting quality and computational costs. We make our code publicly available, so that readers can readily access a wide range of forecasting tools, and possibly use them as benchmarks for more advanced solutions.

This study sought to identify the most accurate forecasting models for COVID-19-confirmed cases, deaths, and recovered patients in Pakistan. For COVID-19, time series data are available from 16 April to 15 August 2021 from the Ministry of National Health Services Regulation and Coordination’s health advice portal. Descriptive as well as time series models, autoregressive integrated moving average, exponential smoothing models (Brown, Holt, and Winters), neural networks, and Error, Trend, Seasonal (ETS) models were applied. The analysis was carried out using the R coding language. The descriptive analysis shows that the average number of confirmed cases, COVID-19-related deaths, and recovered patients reported each day were 2,916, 69.43, and 2,772, respectively. The highest number of COVID-19 confirmed cases and fatalities per day, however, were recorded on April 17, 2021 and April 27, 2021, respectively. ETS (M, N, M), neural network, nonlinear autoregressive (NNAR) (3, 1, 2), and NNAR (8, 1, 4) forecasting models were found to be the best among all other competing models for the reported confirmed cases, deaths, and recovered patients, respectively. COVID-19-confirmed outbreaks, deaths, and recovered patients were predicted to rise on average by around 0.75, 5.08, and 19.11% daily. These statistical results will serve as a guide for disease management and control.

The techniques employed to manage an industrial project are based on tools that aim to achieve the objectives set by an organization. Most of these techniques consider the development of operative and predictive models. The difficulty in developing project planning models relies on estimating large sets of parameters and the need to include model sections of poorly identifiable, that increase costs and time. This work develops a hybrid forecasting model for all the phases that make up die-casting projects through a series of parameters and sub-models that contemplate the particularities of each case, thereby achieving greater precision in the forecast. The model identifies the cost and time factors that affect project planning, specifically in the die-casting industry, and intends to predict their future behaviour when certain initially given conditions are modified. To estimate the parameters of the hybrid model, several factors in the processes were considered that interact in this industry, such as primary matter costs and activities associated to the process. The considered processes that have a substantial economic impact on the implementation of the project were selected. The criteria for this selection considered identifying the relevant parts of the design and manufacturing in the diecasting industry. Process factors such as the Cost of aluminium and its related activities, whose processes will be grouped into cost and time entities to build a set of metrics that allow better control over them. Finally, the proposed model is based on analytical, parametric, and analog methods that achieve accuracy greater than 85 % in predicting the time and Cost of the process. [ABSTRACT FROM AUTHOR]

In this paper, Machine Learning (ML) based techniques known as Support Vector Regression (SVR) and Gradient Boosting Regression Trees (GBRT) are utilized for improving the forecast accuracy of short-term wind power production. In addition, data analysis framework is also prepared to visualize the data acquired from the Supervisory Control and Data Acquisition (SCADA) system. Dataset analysis has been carried out in Polar and Cartesian coordinates to identify the produced power and wind relationship. Based upon different statistical indices, the performance of the developed model has been evaluated. In addition, the comparative analysis reveals that GBRT model performance is superior than that of the SVR model with Mean-Square Error (MSE) value of 0.026, Root Mean-Square Error (RMSE) 0.301, Mean Absolute Percentage Error (MAPE) 0.063, Mean Absolute Error (MAE) 0.004 and Coefficient of determination (R2) 0.969. The results obtained illustrate that the proposed model would be applicable in real-life scenarios and help the management team to handle the produced power from wind turbines. [ABSTRACT FROM AUTHOR]

This paper shows that some commodity currencies (from Chile, Iceland, Norway, South Africa, Australia, Canada, and New Zealand) predict the synchronization of metals and energy commodities. This relationship links the present-value theory for exchange rates and its connection with commodity export economies' fundamentals, where prospective commodity price fluctuations affect exchange rates. Predicting commodity market return synchronization is critical for dealing with systemic risk, market efficiency, and financial stability since synchronization reduces the benefits of diversification and increases the probability of contagion in financial markets during economic and financial crises. Using network methods coupled with in-sample and out-of-sample econometrics models, we find evidence that a fall in the return of commodity-currencies (dollar appreciation) predicts an increase in commodity market synchronization and, consequently, in commodity market systemic risk. This discovery is consistent with a transitive capacity phenomenon, suggesting that commodity currencies have a predictive ability over commodities that extend beyond the commodity bundle that a country produces. The latter behavior would be exacerbated by the high financialization of commodities and strong co-movement of commodity markets. Our paper is part of a vigorously growing literature that has recently measured and predicted systemic risk caused by synchronization, combining a complex systems perspective and financial network analysis. [ABSTRACT FROM AUTHOR]

Solar energetic particles (SEPs) are produced by solar eruptions and are harmful to spacecraft and astronauts. The four source function parameters of particle injection for SEP events and the magnetic turbulence level can be collectively referred to as key parameters. We reproduce the electron intensity-time profiles with simulations for five SEP events observed by multispacecraft such as ACE, STEREO-A, and STEREO-B, so we can obtain the five fitted key parameters for each of the events. We analyze the relationship among the five fitted key parameters, and also the relationship between these parameters and the observed event features. Thus, the model of key parameters are established. Next, we simulate another 12 SEP events with the key parameters model. Though the predicted electron intensity-time profiles do not fit the observed ones well, the peak flux and event-integrated fluence can be predicted accurately. Therefore, the model can be used to estimate the radiation hazards. [ABSTRACT FROM AUTHOR]

We study the latitudinal extent of the near-relativistic electron events of 10 June 2000 and 26 December 2001, observed by both Ulysses and ACE. From the observations it is shown that the intensity of ACE was quite different from that of Ulysses. Through the numerical simulations, we obtain the SEPs time-intensity profiles, which generally fit well to the observations. To compare the observations we obtained the best fit parameters for the simulations. We suggest that the transport effects, especially the perpendicular diffusion effect, can cause the difference between the intensity profiles of ACE and Ulysses, which is dominated by particle transport at a large radial distance and high-latitude when a spacecraft has poor magnetic connection to the particle source. Furthermore, we present the particle source from the best fit parameters to show that the start and peak times of the particle sources are between the onset and max times of a flare in all the energy channels. Moreover, we propose models for the peak intensity and half width of the particle source, and the time interval from the flare onset to the particle source peak time. We show that the models generally agree with the best fit parameters. [ABSTRACT FROM AUTHOR]

The incorporation of renewable energy systems in the world energy system has been steadily increasing during the last few years. In terms of the building sector, the usual consumers are becoming increasingly prosumers, and the trend is that communities of energy, whose households share produced electricity, will increase in number in the future. Another observed tendency is that the aggregator (the entity that manages the community) trades the net community energy in public energy markets. To accomplish economically good transactions, accurate and reliable forecasts of the day-ahead net energy community must be available. These can be obtained using an ensemble of multi-step shallow artificial neural networks, with prediction intervals obtained by the covariance algorithm. Using real data obtained from a small energy community of four houses located in the southern region of Portugal, one can verify that the deterministic and probabilistic performance of the proposed approach is at least similar, typically better than using complex, deep models.

Various techniques have been researched and introduced in water treatment plants to optimise treatment and management processes. This paper presents a solution that can help treatment plants to work more effectively and reach their mine water management goals. Using Python 3.7.1 programming language within an Anaconda 4.11.0 platform, neural networks and regression tree algorithms were compared to find the best performing model after the data had undergone robust data pre-processing and exploratory data analysis statistical techniques. The main aim was to use this best performing model to forecast mining influenced water (MIW) parameters. This approach will help the treatment plant operators in knowing the future MIW chemistry, and they can eventually plan ahead of time what chemicals and methods to use to treat and manage polluted MIW. Westrand mine pool water near Randfontein, South Africa is used as a case study, in which historical data (2016–2021) from shaft № 9 is used to train and test the algorithms. These algorithms included the artificial neural network (ANN), deep neural network (DNN), gradient boosting and random forest regression trees, while the multivariate long short-term memory (LSTM) was used to generate new data for the best performing algorithm. Different data pre-processing approaches were explored, including data interpolation and anomaly detection. These processes were carried out to highlight the most important part of completing a machine learning related project, which is data analytics. Finally, the random forest regression tree algorithm showed the overall best performance and was used to forecast Fe and acidity concentrations of MIW for 60 days. It could be shown that artificial intelligence techniques are capable to optimise and forecast mine water treatment plant parameters, and it is imperative to perform robust statistical analysis on the data before attempting to build forecasting models.