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Copyright of Panorama Económico is the property of Universidad de Cartagena and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As precision agriculture (PA) advances, the demand for accurate and high-resolution weather forecasts becomes critical for optimizing agricultural management practices. Despite improvements in Numerical Weather Prediction (NWP) models, they lack the granularity and efficiency needed for PA. Data-driven models offer a promising alternative by integrating predictive capabilities closer to IoT edge data sources, but their efficacy requires evaluation. Here, this paper evaluates six models from three data-driven eras (statistical, machine learning, and deep learning) using agrometeorological data from an Automatic Weather Station (AWS) in Sidi Rahal, East Marrakech, central Morocco, covering 2013–2020 at half-hour intervals, including air temperature, solar radiation, and relative humidity. First, the data is quality-controlled through imputation using ERA5-Land. Then, the dataset was split into training (2013–2019) and evaluation (2020) sets, with validation horizons of 1 day, 3 days, and 1 week. Statistical models generally perform well in air temperature forecasting, occasionally surpassing other models. However, the Temporal Convolutional Neural Network (TCNN) consistently demonstrates superior performance for challenging variables, balancing low RMSE and high R2 across various horizons, with some exceptions. Specifically, for relative humidity, the linear regression model achieves slightly lower RMSE (3,96% and 6,05%) compared to TCNN (4,00% and 6,79%) for 1 day and 3 days, respectively. Additionally, CatBoost outperforms TCNN for 1-week forecasts. In terms of training time, the Transformer requires the longest, followed by AutoARIMA and CatBoost. Uncertainty analysis of stochastic models using solar radiation showed the stable performance of TCNN with 0,80 and 0,01 for the RMSE and R2 standard deviations, respectively. Considering the trade-off between performance, training time, and capturing complex relationships, TCNN emerges as the optimal choice. ANOVA, Tukey's HSD and Mann-Whitney U statistical tests also confirmed TCNN's performance. Finally, a comparison with the Global Forecast System (GFS) reveals TCNN's clear superiority in all metrics, particularly evident for the RMSE of 3 days air temperature forecasts (TCNN: 1,96 °C, GFS: 3,59 °C). [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, a group of health program implementors and research analysts across seven low- and middle-income countries (LMICs) alongside Boston-based collaborators convened to implement data-driven approaches for public health response. An intensive statistics and data science training short course was developed to ensure that in-country researchers could implement the necessary statistical methods for COVID-19 surveillance. The main goal of the course was to enable interpretation of findings from time series analyses and flag potential data issues. This manuscript summarizes our experience teaching this course, including a detailed course overview, participant feedback, and thoughts on how targeted, online courses can be used to support statistical capacity building in LMICs. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, a group of health program implementors and research analysts across seven low- and middle-income countries (LMICs) alongside Boston-based collaborators convened to implement data-driven approaches for public health response. An intensive statistics and data science training short course was developed to ensure that in-country researchers could implement the necessary statistical methods for COVID-19 surveillance. The main goal of the course was to enable interpretation of findings from time series analyses and flag potential data issues. This manuscript summarizes our experience teaching this course, including a detailed course overview, participant feedback, and thoughts on how targeted, online courses can be used to support statistical capacity building in LMICs.

Abstract Air quality is a fundamental component of a healthy environment for human beings. Monitoring networks for air pollution have been established in numerous industrial zones. The data collected by the pervasive monitoring devices can be utilized not only for determining the current environmental condition, but also for forecasting it in the near future. This paper considers the applications of different machine learning methods for the prediction of the two most widely used quantities. Particulate matter (PM) with a diameter of 2.5 and 10 µm, respectively. The data are collected via a proprietary monitoring station, designated as the Ecolumn. The Ecolumn monitors a number of key parameters, including temperature, pressure, humidity, PM 1.0, PM 2.5, and PM 10, in a timely manner. The data were employed in the development of multiple models based on selected machine learning methods. The decision tree, random forest, recurrent neural network, and long short-term memory models were employed. Experiments were conducted with varying hyperparameters and network architectures. Different time scales (10 min, 1 h, and 24 h) were examined. The most optimal results were observed for the Long Short-Term Memory algorithm when utilizing the shortest available time spans (shortest averaging times). The decision tree and random forest algorithms demonstrated unexpectedly high performance for long averaging times, exhibiting only a slight decline in accuracy compared to neural networks for shorter averaging times. Recommendations for the potential applicability of the tested methods were formulated.

Air quality is a fundamental component of a healthy environment for human beings. Monitoring networks for air pollution have been established in numerous industrial zones. The data collected by the pervasive monitoring devices can be utilized not only for determining the current environmental condition, but also for forecasting it in the near future. This paper considers the applications of different machine learning methods for the prediction of the two most widely used quantities. Particulate matter (PM) with a diameter of 2.5 and 10 µm, respectively. The data are collected via a proprietary monitoring station, designated as the Ecolumn. The Ecolumn monitors a number of key parameters, including temperature, pressure, humidity, PM 1.0, PM 2.5, and PM 10, in a timely manner. The data were employed in the development of multiple models based on selected machine learning methods. The decision tree, random forest, recurrent neural network, and long short-term memory models were employed. Experiments were conducted with varying hyperparameters and network architectures. Different time scales (10 min, 1 h, and 24 h) were examined. The most optimal results were observed for the Long Short-Term Memory algorithm when utilizing the shortest available time spans (shortest averaging times). The decision tree and random forest algorithms demonstrated unexpectedly high performance for long averaging times, exhibiting only a slight decline in accuracy compared to neural networks for shorter averaging times. Recommendations for the potential applicability of the tested methods were formulated. [ABSTRACT FROM AUTHOR]

Decision trees constitute a simple yet powerful and interpretable machine learning tool. While tree-based methods are designed only for cross-sectional data, we propose an approach that combines decision trees with time series modeling and thereby bridges the gap between machine learning and statistics. In particular, we combine decision trees with hidden Markov models where, for any time point, an underlying (hidden) Markov chain selects the tree that generates the corresponding observation. We propose an estimation approach that is based on the expectation-maximisation algorithm and assess its feasibility in simulation experiments. In our real-data application, we use eight seasons of National Football League (NFL) data to predict play calls conditional on covariates, such as the current quarter and the score, where the model's states can be linked to the teams' strategies. R code that implements the proposed method is available on GitHub. [ABSTRACT FROM AUTHOR]

Cool‐water habitats provide increasingly vital refuges for cold‐water fish living on the margins of their historical ranges; consequently, efforts to enhance or create cool‐water habitat are becoming a major focus of river restoration practices. However, the effectiveness of restoration projects for providing thermal refuge and creating diverse temperature regimes at the watershed scale remains unclear. In the Klamath River in northern California, the Karuk Tribe Fisheries Program, the Mid‐Klamath Watershed Council, and the U.S. Forest Service constructed a series of off‐channel ponds that recreate floodplain habitat and support juvenile coho salmon (Oncorhynchus kisutch) and steelhead (O. mykiss) along the Klamath River and its tributaries. We instrumented these ponds and applied multivariate autoregressive time series models of fine‐scale temperature data from ponds, tributaries, and the mainstem Klamath River to assess how off‐channel ponds contributed to thermal regime diversity and thermal refuge habitat in the Klamath riverscape. Our analysis demonstrated that ponds provide diverse thermal habitats that are significantly cooler than creek or mainstem river habitats, even during severe drought. Wavelet analysis of long‐term (10 years) temperature data indicated that thermal buffering (i.e. dampening of diel variation) increased over time but was disrupted by drought conditions in 2021. Our analysis demonstrates that in certain situations, human‐made off‐channel ponds can increase thermal diversity in modified riverscapes even during drought conditions, potentially benefiting floodplain‐dependent cold‐water species. Restoration actions that create and maintain thermal regime diversity and thermal refuges will become an essential tool to conserve biodiversity in climate‐sensitive watersheds. [ABSTRACT FROM AUTHOR]

The imbalanced I/O load on large parallel file systems affects the parallel I/O performance of high-performance computing (HPC) applications. One of the main reasons for I/O imbalances is the lack of a global view of system-wide resource consumption. While approaches to address the problem already exist, the diversity of HPC workloads combined with different file striping patterns prevents widespread adoption of these approaches. In addition, load-balancing techniques should be transparent to client applications. To address these issues, we propose Tarazu, an end-to-end control plane where clients transparently and adaptively write to a set of selected I/O servers to achieve balanced data placement. Our control plane leverages real-time load statistics for global data placement on distributed storage servers, while our design model employs trace-based optimization techniques to minimize latency for I/O load requests between clients and servers and to handle multiple striping patterns in files. We evaluate our proposed system on an experimental cluster for two common use cases: the synthetic I/O benchmark IOR and the scientific application I/O kernel HACC-I/O. We also use a discrete-time simulator with real HPC application traces from emerging workloads running on the Summit supercomputer to validate the effectiveness and scalability of Tarazu in large-scale storage environments. The results show improvements in load balancing and read performance of up to 33% and 43%, respectively, compared to the state-of-the-art. [ABSTRACT FROM AUTHOR]

Generative adversarial networks (GANs) have been extremely successful in generating samples, from seemingly high‐dimensional probability measures. However, these methods struggle to capture the temporal dependence of joint probability distributions induced by time‐series data. Furthermore, long time‐series data streams hugely increase the dimension of the target space, which may render generative modeling infeasible. To overcome these challenges, motivated by the autoregressive models in econometric, we are interested in the conditional distribution of future time series given the past information. We propose the generic conditional Sig‐WGAN framework by integrating Wasserstein‐GANs (WGANs) with mathematically principled and efficient path feature extraction called the signature of a path. The signature of a path is a graded sequence of statistics that provides a universal description for a stream of data, and its expected value characterizes the law of the time‐series model. In particular, we develop the conditional Sig‐W1$W_1$ metric that captures the conditional joint law of time series models and use it as a discriminator. The signature feature space enables the explicit representation of the proposed discriminators, which alleviates the need for expensive training. We validate our method on both synthetic and empirical dataset and observe that our method consistently and significantly outperforms state‐of‐the‐art benchmarks with respect to measures of similarity and predictive ability. [ABSTRACT FROM AUTHOR]

This paper deals with Network Slicing-based 5G Networks. A network slice can be defined as a set of network and virtual network function (VNF) resources deployed across multiple independent infrastructure provider domains. Such multi-domain 5G deployments pose challenges since the domains would not share internal resource allocation details. Slice demands and QoS requirements tend to vary dynamically and must be satisfied by scaling the allocated VNF resources. In this paper, we use the federated learning approach in which the training data remains within the respective domains, while the system learns a shared model by aggregating locally-computed updates. Two state-of-the-art deep learning models, namely Long Short-Term Memory (LSTM) and Gated recurrent units (GRU) are used for forecasting. We present a comparison of the performance of the proposed federated system with the centralized system. Further, synthetic data in each domain has been generated using Generative Adversarial Networks (GAN) to improve the forecasting results. A Python-based discrete event simulator model of the proposed auto-scaling system was written. The experiments were used to study the performance of scaling and non-scaling systems across various workloads, and proactive approach’s effectiveness over reactive scaling. The average queue length was reduced by scaling, compared to a non-scaling system, by around 75% to 82% for different traffic type mixes. The results show that the proactive scaling system outperformed the reactive system in terms of the time required to set up new instances by around 70%.

Electricity demand forecasting is crucial for practical power system management. However, during the COVID-19 pandemic, the electricity demand system deviated from normal system, which has detrimental bias effect in future forecasts. To overcome this problem, we propose a deep learning framework with a COVID-19 adjustment for electricity demand forecasting. More specifically, we first designed COVID-19 related variables and applied a multiple linear regression model. After eliminating the impact of COVID-19, we employed an efficient deep learning algorithm, long short-term memory multiseasonal net deseasonalized approach, to model residuals from the linear model aforementioned. Finally, we demonstrated the merits of the proposed framework using the electricity demand in Taixing, Jiangsu, China, from May 13, 2018 to August 2, 2021.

Timely forecasting of aboveground vegetation biomass is crucial for effective management and ensuring food security. However, research on predicting aboveground biomass remains scarce. Artificial intelligence (AI) methods could bridge this research gap and provide early warning to planners and stakeholders. This study evaluates the effectiveness of deep learning (DL) algorithms in predicting aboveground vegetation biomass with limited-size data. It employs an iterative forecasting procedure for four target horizons, comparing the performance of DL models—multi-layer perceptron (MLP), long short-term memory (LSTM), gated recurrent unit (GRU), convolutional neural network (CNN), and CNN-LSTM—against the traditional seasonal autoregressive integrated moving average (SARIMA) model, serving as a benchmark. Five limited-size vegetation biomass time series from Kenyan grasslands with values at 15-day intervals over a 20-year period were chosen for this purpose. Comparing the outcomes of these models revealed significant differences (p < 0.05); however, none of the models proved superior among the five time series and the four horizons evaluated. The SARIMA, CNN, and CNN-LSTM models performed best, with the statistical model slightly outperforming the other two. Additionally, the accuracy of all five models varied significantly according to the prediction horizon (p < 0.05). As expected, the accuracy of the models decreased as the prediction horizon increased, although this relationship was not strictly monotonic. Finally, this study indicated that, in limited-size aboveground vegetation biomass time series, there is no guarantee that deep learning methods will outperform traditional statistical methods. [ABSTRACT FROM AUTHOR]

Time series modeling is a way to predict future values by examining temporal data. The present study analyzes the monthly mean soil moisture data at various depths: surface, profile, and root soil moisture, spanning from 1981 to 2022. The analysis employs two distinct approaches: the statistical seasonal autoregressive integrated moving average (SARIMA) and a deep learning long short-term memory (LSTM). The models are trained on a data set, covering the period from 1981 to 2021, acquired from the agricultural site at Andhra Loyola College in Vijayawada, Andhra Pradesh, India. Subsequently, the data from 2021 to 2022 is reserved for testing purposes. The study provides comprehensive insights into the design of both SARIMA and LSTM models, along with an evaluation of their performance using established error metrics such as the model mean absolute percentage error (MAPE), mean absolute error (MAE), and root mean squared error (RMSE). In the context of surface soil moisture prediction, the LSTM model demonstrates superior performance compared to SARIMA. Specifically, LSTM achieves a notably lower MAPE of 0.0615 in contrast to SARIMA's 0.1541, a reduced MAE of 0.0316 compared to 0.0871, and a diminished RMSE of 0.0412 as opposed to 0.1021. This pattern of enhanced accuracy persists across profile and root soil moisture predictions, further establishing LSTM's supremacy in predictive capability across diverse soil moisture levels. [ABSTRACT FROM AUTHOR]

Background: Through the fifth wave of the Covid-19 outbreak in Jahrom, the fatality and incidence of the virus increased. The quick spread of infection is one of the causes of this dreadful situation. Therefore, recognizing the future epidemic trend can be a useful instrument to decrease mortality and morbidity. This study aimed to determine the time trends and select the best model to predict the sixth wave of the COVID-19 outbreak using ARIMA models.Methods: We used daily data of 9533 hospital cases (Suspected and PCR-confirmed COVID-19 cases) between 4th March 2020 and 31st December 2021. Nine different ARIMA models were fitted to our data. Autocorrelation functions (ACF) and partial autocorrelation (PACF) plots were used to determine model parameters. Likelihood-ratio test for comparison of the reduced and full model was used. In addition, Akaike Information Criteria (AIC) was also used to choose the final model. Data were analyzed by STATA 14 software with a significant level of 0.05.Results: The ARIMA (3, 0, 3) model was selected among the potential models, with lower AIC (999) and MAPE (3.18%) values. This model showed that the daily number of hospitalized patients may increase from 5.85 (2.16-15.79) to 8.55 (1.47-49.48) in two months. By March 01, 2022, the predictable daily hospitalized cases could reach 468.36 (03.79-2209.88).Conclusion: Time series models is a useful tool for predictingthe hospitals’ admission trend during an epidemic. Thus, they can be used as early warning models in the readiness of hospital systems during epidemics.

Background The COVID-19 pandemic has disrupted health services worldwide, which may have led to increased mortality and secondary disease outbreaks. Disruptions vary by patient population, geographic area, and service. While many reasons have been put forward to explain disruptions, few studies have empirically investigated their causes. Objective We quantify disruptions to outpatient services, facility-based deliveries, and family planning in seven low- and middle-income countries during the COVID-19 pandemic and quantify relationships between disruptions and the intensity of national pandemic responses. Methods We leveraged routine data from 104 Partners In Health-supported facilities from January 2016 to December 2021. We first quantified COVID-19-related disruptions in each country by month using negative binomial time series models. We then modelled the relationship between disruptions and the intensity of national pandemic responses, as measured by the stringency index from the Oxford COVID-19 Government Response Tracker. Results For all the studied countries, we observed at least one month with a significant decline in outpatient visits during the COVID-19 pandemic. We also observed significant cumulative drops in outpatient visits across all months in Lesotho, Liberia, Malawi, Rwanda, and Sierra Leone. A significant cumulative decrease in facility-based deliveries was observed in Haiti, Lesotho, Mexico, and Sierra Leone. No country had significant cumulative drops in family planning visits. For a 10-unit increase in the average monthly stringency index, the proportion deviation in monthly facility outpatient visits compared to expected fell by 3.9% (95% CI: −5.1%, −1.6%). No relationship between stringency of pandemic responses and utilisation was observed for facility-based deliveries or family planning. Conclusions Context-specific strategies show the ability of health systems to sustain essential health services during the pandemic. The link between pandemic responses and healthcare utilisation can inform purposeful strategies to ensure communities have access to care and provide lessons for promoting the utilisation of health services elsewhere.

Copyright of Social Sciences Studies is the property of Social Sciences Studies and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

An increasing number of U.S. cities require commercial/office properties to publicly disclose their energy performance due to the adoption of energy benchmarking and disclosure policies. This level of transparency provides an additional in-depth assessment of a building's performance beyond a sustainability certification (e.g., Energy Star, LEED) and may lead less energy-efficient buildings to invest in energy retrofits, therefore improving their marketability. However, the research is scarce on assessing the impact of such policies on office building marketability. This study tries to fill this gap by investigating the impact of energy benchmarking policies on the performance of office buildings in four major U.S. cities (New York; Washington, D.C.; San Francisco; and Chicago). We use interrupted time series analysis (ITSA), while accounting for sustainability certification, public policy adoption, and property real estate performance. The results revealed that in some cities, energy-efficient buildings generally perform better than less energy-efficient buildings after the policy implementation, especially if they are Class A. The real estate performances of energy-efficient buildings also exhibited continuously increasing trends after the policy implementation. However, due to potentially confounding factors, further analysis is required to conclude the policy impacts on energy-efficient buildings are more positive than those on less energy-efficient buildings. [ABSTRACT FROM AUTHOR]

Groups of interacting individuals often coordinate in service ofabstract goals, such as the alignment of mental representationsin conversation, or the generation of new ideas in group brain-storming sessions. What are the mechanisms and dynamicsof abstract coordination? This study examines coordination ina sophisticated paragon domain: collaboratively improvisingjazz musicians. Remarkably, freely improvising jazz ensem-bles collectively produce coherent tonal structure (i.e. melodyand harmony) in real time performance without previously es-tablished harmonic forms. We investigate how tonal structureemerges out of interacting musicians, and how this structureis constrained by underlying patterns of coordination. Dyadsof professional jazz pianists were recorded improvising in twoconditions of interaction: a ‘coupled’ condition in which theycould mutually adapt to one another, and an ‘overdubbed’ con-dition which precluded mutual adaptation. Using a computa-tional model of musical tonality, we show that this manipu-lation effected the directed flow of tonal information amongstpianists, who could mutually adapt to one another’s notes incoupled trials, but not in overdubbed trials. Consequently,musicians were better able to harmonize with one another incoupled trials, and this ability increased throughout the courseof improvised performance. We present these results and dis-cuss their implications for music technology and joint actionresearch more generally.

In this paper, an architecture based on computational intelligence for time series modeling is proposed to guarantee the automatic adjustability of trained models no matter the dynamic behavior of the modeled phenomena. Time series are widely used to plan and execute operational and strategic tasks related to the need of forecasting phenomena. Several conventional and non-conventional techniques have been studied for time series modeling. However, the model performance and metrics are affected by non-stationary behaviors. In addition, determining effectively when a model fails can be problematic because the Mean Absolute Percentage Error (MAPE) metric does not necessarily reveal changes in the model predicted curve. Therefore, a novel metric to assess the performance is proposed; and then, an effective maintenance routine for the time-series model is properly devised. Thus, an auditor is created to identify when a model must be updated before losing forecast performance. Hence, using the defined rMAPE performance metric, the auditor output trustworthy detects if the updating process does not achieve better performance, and if replacing a time-series model is required. It is important to note that the devised scheme counts with several assemblies in a local knowledge base. The intelligent system allows building time-series models automatically considering exogenous variables such as weather, calendar, and statistical transformations that can lead to the number of models required for a particular application. The proposed approach has been experimentally tested for power consumption and energy price via simulation. The forecasting results showed an improvement in the MAPE of up to 23% in the tests performed.

Abstract This paper proposes an improved deep learning‐based maximum power point tracking (MPPT) in solar photovoltaic cells considering various time series‐based environmental inputs. Generally, artificial neural network‐based MPPT algorithms use basic neural network architectures and inputs which do not represent the ambient conditions in a comprehensive manner. In this article, the ambient conditions of a location are represented through a comprehensive set of environmental features. Furthermore, the inclusion of time‐based features in the input data is considered to model cyclic patterns temporally within the atmospheric conditions leading to robust modeling of the MPPT algorithm. A transformer‐based deep learning architecture is trained as a time series prediction model using multidimensional time series input features. The model is trained on a dataset containing typical meteorological‐year data points of ambient weather conditions from 50 locations. The attention mechanism in the transformer modules allows the model to learn temporal patterns in the data efficiently. The proposed model achieves a 0.47% mean average percentage error of prediction on non‐zero operating voltage points in a test dataset consisting of data collected over a period of 200 consecutive hours; resulting in the average power efficiency of 99.54% and peak power efficiency of 99.98%. The proposed model is validated through real‐time simulations. The proposed model performs power point tracking in a robust, dynamic, and nonlatent manner, over a wide range of atmospheric conditions.

Machine learning (ML) models, including artificial neural networks (ANN), generalized neural regression networks (GRNN), and adaptive neuro-fuzzy interface systems (ANFIS), have received considerable attention for their ability to provide accurate predictions in various problem domains. However, these models may produce inconsistent results when solving linear problems. To overcome this limitation, this paper proposes hybridizations of ML and autoregressive integrated moving average (ARIMA) models to provide a more accurate and general forecasting model for evapotranspiration (ET0). The proposed models are developed and tested using daily ET0 data collected over 11 years (2010–2020) in the Samsun province of Türkiye. The results show that the ARIMA–GRNN model reduces the root mean square error by 48.38%, the ARIMA–ANFIS model by 8.56%, and the ARIMA–ANN model by 6.74% compared to the traditional ARIMA model. Consequently, the integration of ML with ARIMA models can offer more accurate and dependable prediction of daily ET0, which can be beneficial for many branches such as agriculture and water management that require dependable ET0 estimations. [ABSTRACT FROM AUTHOR]

Copyright of Forestry Chronicle is the property of Canadian Institute of Forestry and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Timely forecasting of aboveground vegetation biomass is crucial for effective management and ensuring food security. However, research on predicting aboveground biomass remains scarce. Artificial intelligence (AI) methods could bridge this research gap and provide early warning to planners and stakeholders. This study evaluates the effectiveness of deep learning (DL) algorithms in predicting aboveground vegetation biomass with limited-size data. It employs an iterative forecasting procedure for four target horizons, comparing the performance of DL models—multi-layer perceptron (MLP), long short-term memory (LSTM), gated recurrent unit (GRU), convolutional neural network (CNN), and CNN-LSTM—against the traditional seasonal autoregressive integrated moving average (SARIMA) model, serving as a benchmark. Five limited-size vegetation biomass time series from Kenyan grasslands with values at 15-day intervals over a 20-year period were chosen for this purpose. Comparing the outcomes of these models revealed significant differences (p < 0.05); however, none of the models proved superior among the five time series and the four horizons evaluated. The SARIMA, CNN, and CNN-LSTM models performed best, with the statistical model slightly outperforming the other two. Additionally, the accuracy of all five models varied significantly according to the prediction horizon (p < 0.05). As expected, the accuracy of the models decreased as the prediction horizon increased, although this relationship was not strictly monotonic. Finally, this study indicated that, in limited-size aboveground vegetation biomass time series, there is no guarantee that deep learning methods will outperform traditional statistical methods.

The calculation of time series data in determining and forecasting the dynamics of livestock growth requires the use of econometric models. Currently, experts have extensive experience and knowledge in the application of univariate correlation and regression analysis in the processing of statistical data. The state is discussing the problems of choosing a model and confirming its adequacy in assessing and forecasting the state of the livestock, and also constructing equations that satisfy its econometric criteria. The results have been confirmed; the models developed by the authors can be used to predict the number of livestock and poultry, both in the preliminary results of the EAEU and in our republic.

Accurate prediction of fire spread is considered crucial for facilitating effective fire management, enabling proactive planning, and efficient allocation of resources. This study places its focus on wildfires in two regions of Alberta, Fort McMurray and Slave Lake, in Southwest Canada. For the simulation of wildfire spread, an adapted fire propagation model was employed, incorporating MODIS datasets such as land surface temperature, land cover, land use, and integrated climate data. The pixels were classified as burned or unburned in relation to the 2011 Slave Lake wildfire and the initial 16 days of the 2016 Fort McMurray wildfire, utilizing defined starting points and the aforementioned specified datasets. The simulation for the 2011 Slave Lake wildfire achieved an weighted average precision, recall, and f1-scores of 0.989, 0.986, and 0.987, respectively. Additionally, macro-averaged scores across these three phases were 0.735, 0.829, and 0.774 for precision, recall, and F1-scores, respectively. The simulation of the 2016 Fort McMurray wildfire introduced a phased analysis, dividing the initial 16 days into three distinct periods. This approach led to average precision, recall, and f1-scores of 0.958, 0.933, and 0.942 across these phases. Additionally, macro-averaged scores across these three phases were 0.681, 0.772, and 0.710 for precision, recall, and F1-scores, respectively. The strategy of segmenting simulations into phases may enhance adaptability to dynamic factors like weather conditions and firefighting strategies.

The electricity market is particularly complex due to the different arrangements and structures of its participants. If the energy price in this market presents in a conceptual and well-known way, the complexity of the market will be greatly reduced. Drastic changes in the supply and demand markets are a challenge for electricity prices (EPs), which necessitates the short-term forecasting of EPs. In this study, two restructured power systems are considered, and the EPs of these systems are entirely and accurately predicted using a Gaussian process (GP) model that is adapted for time series predictions. In this modeling, various models of the GP, including dynamic, static, direct, and indirect, as well as their mixture models, are used and investigated. The effectiveness and accuracy of these models are compared using appropriate evaluation indicators. The results show that the combinations of the GP models have lower errors than individual models, and the dynamic indirect GP was chosen as the best model.

The paper introduces two novel algorithms for predicting and propagating drought stress in plants using image sequences captured by cameras in two modalities, i.e., visible light and hyperspectral. The first algorithm, VisStressPredict, computes a time series of holistic phenotypes, e.g., height, biomass, and size, by analyzing image sequences captured by a visible light camera at discrete time intervals and then adapts dynamic time warping (DTW), a technique for measuring similarity between temporal sequences for dynamic phenotypic analysis, to predict the onset of drought stress. The second algorithm, HyperStressPropagateNet, leverages a deep neural network for temporal stress propagation using hyperspectral imagery. It uses a convolutional neural network to classify the reflectance spectra at individual pixels as either stressed or unstressed to determine the temporal propagation of stress in the plant. A very high correlation between the soil water content, and the percentage of the plant under stress as computed by HyperStressPropagateNet on a given day demonstrates its efficacy. Although VisStressPredict and HyperStressPropagateNet fundamentally differ in their goals and hence in the input image sequences and underlying approaches, the onset of stress as predicted by stress factor curves computed by VisStressPredict correlates extremely well with the day of appearance of stress pixels in the plants as computed by HyperStressPropagateNet. The two algorithms are evaluated on a dataset of image sequences of cotton plants captured in a high throughput plant phenotyping platform. The algorithms may be generalized to any plant species to study the effect of abiotic stresses on sustainable agriculture practices. [ABSTRACT FROM AUTHOR]

Labor costs constitute a significant portion of construction costs. Reliable forecasts provide insight into the movements of labor costs and are critical to the success of projects. Past studies have primarily focused on forecasting construction cost indexes or material costs. Only a few studies have concentrated on forecasting construction labor costs. This study presents a multivariate Bayesian structural time series (MBSTS) model to characterize the future values of construction labor's average hourly earnings (AHE) using a set of candidate predictors. The methodologies commonly used by past studies do not adequately address the uncertainties associated with the modeling process. In contrast, MBSTS recognizes the uncertainty in its modeling process, which enables practitioners to quantify and account for future labor cost risks in decisions. Furthermore, the MBSTS method results in a transparent model, helping analysts investigate the rationality of the parameters. This article trains MBSTS models under four different data subset lengths (i.e., 150, 144, 138, and 132 months) to study the consistency of the explanatory variables and their corresponding coefficients. The analysis results indicate that the gross domestic product (GDP), housing starts (HS), number of building permits (BP), Construction Cost Index (CCI), Dow Jones Industrial Average (DJI), and Standard and Poor's 500 index (SPI) are the most frequently used predictors in the regression component of the MBSTS models. The results indicate an inverse relationship between AHE from one side and HS and BP from the other. Likewise, a direct relationship exists between AHE and GDP, CCI, DJI, and SPI. The MBSTS model performed well on the validation subset in the midrange prediction intervals (i.e., 12- and 18-month periods). However, it was outperformed by conventional time series models [i.e., seasonal autoregressive integrated moving average (SARIMA)] when used for short-term forecasting. The proposed framework can be applied to facilitate monetary resource allocation in projects. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has disrupted health services worldwide, which may have led to increased mortality and secondary disease outbreaks. Disruptions vary by patient population, geographic area, and service. While many reasons have been put forward to explain disruptions, few studies have empirically investigated their causes. We quantify disruptions to outpatient services, facility-based deliveries, and family planning in seven low- and middle-income countries during the COVID-19 pandemic and quantify relationships between disruptions and the intensity of national pandemic responses. We leveraged routine data from 104 Partners In Health-supported facilities from January 2016 to December 2021. We first quantified COVID-19-related disruptions in each country by month using negative binomial time series models. We then modelled the relationship between disruptions and the intensity of national pandemic responses, as measured by the stringency index from the Oxford COVID-19 Government Response Tracker. For all the studied countries, we observed at least one month with a significant decline in outpatient visits during the COVID-19 pandemic. We also observed significant cumulative drops in outpatient visits across all months in Lesotho, Liberia, Malawi, Rwanda, and Sierra Leone. A significant cumulative decrease in facility-based deliveries was observed in Haiti, Lesotho, Mexico, and Sierra Leone. No country had significant cumulative drops in family planning visits. For a 10-unit increase in the average monthly stringency index, the proportion deviation in monthly facility outpatient visits compared to expected fell by 3.9% (95% CI: −5.1%, −1.6%). No relationship between stringency of pandemic responses and utilisation was observed for facility-based deliveries or family planning. Context-specific strategies show the ability of health systems to sustain essential health services during the pandemic. The link between pandemic responses and healthcare utilisation can inform purposeful strategies to ensure communities have access to care and provide lessons for promoting the utilisation of health services elsewhere. [ABSTRACT FROM AUTHOR]

The paper introduces two novel algorithms for predicting and propagating drought stress in plants using image sequences captured by cameras in two modalities, i.e., visible light and hyperspectral. The first algorithm, VisStressPredict, computes a time series of holistic phenotypes, e.g., height, biomass, and size, by analyzing image sequences captured by a visible light camera at discrete time intervals and then adapts dynamic time warping (DTW), a technique for measuring similarity between temporal sequences for dynamic phenotypic analysis, to predict the onset of drought stress. The second algorithm, HyperStressPropagateNet, leverages a deep neural network for temporal stress propagation using hyperspectral imagery. It uses a convolutional neural network to classify the reflectance spectra at individual pixels as either stressed or unstressed to determine the temporal propagation of stress in the plant. A very high correlation between the soil water content, and the percentage of the plant under stress as computed by HyperStressPropagateNet on a given day demonstrates its efficacy. Although VisStressPredict and HyperStressPropagateNet fundamentally differ in their goals and hence in the input image sequences and underlying approaches, the onset of stress as predicted by stress factor curves computed by VisStressPredict correlates extremely well with the day of appearance of stress pixels in the plants as computed by HyperStressPropagateNet. The two algorithms are evaluated on a dataset of image sequences of cotton plants captured in a high throughput plant phenotyping platform. The algorithms may be generalized to any plant species to study the effect of abiotic stresses on sustainable agriculture practices.

A prominent area of data analytics is "timeseries modeling" where it is possible to forecast future values for the same variable using previous data. Numerous usage examples, including the economy, the weather, stock prices, and the development of a corporation, demonstrate its significance. Experiments with time series forecasting utilizing machine learning (ML), deep learning (DL), and AutoML are conducted in this paper. Its primary contribution consists of addressing the forecasting problem by experimenting with additional ML and DL models and AutoML frameworks and expanding the AutoML experimental knowledge. In addition, it contributes by breaking down barriers found in past experimental studies in this field by using more sophisticated methods. The datasets this empirical research utilized were secondary quantitative data of the real prices of the currently most used cryptocurrencies. We found that AutoML for timeseries is still in the development stage and necessitates more study to be a viable solution since it was unable to outperform manually designed ML and DL models. The demonstrated approaches may be utilized as a baseline for predicting timeseries data. [ABSTRACT FROM AUTHOR]

Aircraft engines must always maintain a margin between the operating equivalence ratio and the lean blowout boundary because flame-out presents a significant risk to the safety of the aircraft. It is believed that flames undergo a series of extinction/re-ignition phenomena before blowout. Previous attempts to characterize these phenomena have not been universally accepted. The approach presented here is from data analytics and consists of three parts: data curation, fault detection, and an adaptive alarm reliability assessment. The data curation filters the nonstationary behavior from photomultiplier tube signals recorded from a combustor test-rig, thereby reducing the number of false alarms. The filtered data is used to develop a fault detection algorithm that detects changes in the statistical properties of the signal. This results in alarms that serve as precursors of impending blowout. By leveraging information from previous blowout occurrences and the currently observed signal, the reliability of these alarms is updated in an adaptive manner. Through this methodology, combustion system operators are provided a means for assessing the proximity of blowout in a probabilistic manner. [ABSTRACT FROM AUTHOR]

This paper proposes an improved deep learning‐based maximum power point tracking (MPPT) in solar photovoltaic cells considering various time series‐based environmental inputs. Generally, artificial neural network‐based MPPT algorithms use basic neural network architectures and inputs which do not represent the ambient conditions in a comprehensive manner. In this article, the ambient conditions of a location are represented through a comprehensive set of environmental features. Furthermore, the inclusion of time‐based features in the input data is considered to model cyclic patterns temporally within the atmospheric conditions leading to robust modeling of the MPPT algorithm. A transformer‐based deep learning architecture is trained as a time series prediction model using multidimensional time series input features. The model is trained on a dataset containing typical meteorological‐year data points of ambient weather conditions from 50 locations. The attention mechanism in the transformer modules allows the model to learn temporal patterns in the data efficiently. The proposed model achieves a 0.47% mean average percentage error of prediction on non‐zero operating voltage points in a test dataset consisting of data collected over a period of 200 consecutive hours; resulting in the average power efficiency of 99.54% and peak power efficiency of 99.98%. The proposed model is validated through real‐time simulations. The proposed model performs power point tracking in a robust, dynamic, and nonlatent manner, over a wide range of atmospheric conditions. [ABSTRACT FROM AUTHOR]

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Among other hospitalization causes and cases, the clinical emergency is a critical case and the data of the reporting patients are biased as well as poorly managed due to the chaotic situation. The world has faced chaos over the past year due to the frequent waves of COVID-19 and the resulting emergencies. The data banks, linked with the clinical emergencies require serious quantitative and qualitative analysis to drive interpretable conclusions for necessary future emergency measures and to develop explainable artificial intelligence tools. This important procedure involves the clear understanding of the data patterns and topologies, which is a great challenge for the multidimensional data sets. Mathematically, the topological mapping can resolve this problem by mapping higher-dimensional data to two-dimensional representation, based on the overall association. Proper data mining and pattern recognition can help in improving the rapid patients admission, in providing the medical resources timely and in proper patient administration. In this paper, the importance of self-organizing maps, to interpret the hospital data, particularly for the COVID-19 epidemic is discussed in detail. Important variables are identified with the aid of networks and mappings. [ABSTRACT FROM AUTHOR]

The accurate estimation of gross primary production (GPP) is crucial to understanding plant carbon sequestration and grasping the quality of the ecological environment. Nevertheless, due to the inconsistencies of current GPP products, the variations, trends and short-term predictions of GPP have not been sufficiently well studied. In this study, we explore the spatiotemporal variability and trends of GPP and its associated climatic and anthropogenic factors in China from 1982 to 2015, mainly based on the optimum light use efficiency (LUEopt) product. We also employ an autoregressive integrated moving average (ARIMA) model to forecast the monthly GPP for a one-year lead time. The results show that GPP experienced an upward trend of 2.268 g C/m2 per year during the studied period, that is, an increasing rate of 3.9% per decade since 1982. However, these trend changes revealed distinct heterogeneity across space and time. The positive trends were mainly distributed in the Yellow River and Huaihe River out of the nine major river basins in China. We found that the dynamics of GPP were concurrently affected by climate factors and human activities. While air temperature and leaf area index (LAI) played dominant roles at a national level, the effects of precipitation, downward shortwave radiation (SRAD), carbon dioxide (CO2) and aerosol optical depth (AOD) exhibited discrepancies in terms of degree and scope. The ARIMA model achieved satisfactory prediction performance in most areas, though the accuracy was influenced by both data values and data quality. The model can potentially be generalized for other biophysical parameters with distinct seasonality. Our findings are further verified and corroborated by four widely used GPP products, demonstrating a good consistency of GPP trends and prediction. Our analysis provides a robust framework for characterizing long-term GPP dynamics that shed light on the improved assessment of the environmental quality of terrestrial ecosystems. [ABSTRACT FROM AUTHOR]

The primary purpose of this study was to demonstrate the role of time series model in predicting process and to pursue analysis of time series data using long-term records of monthly mean sea level from January 1978 to October 2020 at Grand Isle, Louisiana. Following the Box-Jenkins methodology, the ARIMA (1,1,1)(2,0,0) with drift model was selected to be the best fitting model for the time series, according to the lowest AIC value in this study. Empirically, the results revealed that the MLP neural network model performed better compared to the ARIMA(1,1,1)(2,0,0) with drift model at its smaller MSE value. Hence, the MLP neural network model not only can provided information which are important in decision making process related to the future sea level change impacts, but also can be employed in forecasting the future performance for local mean sea level change outcomes. Understanding past sea level is important for the analysis of current and future sea level changes. In order to sustain these observations, research programs utilizing the resulting data should be able to improve significantly our understanding and narrow projections of future sea level rise and variability. [ABSTRACT FROM AUTHOR]

The need for efficient statistical models has increased with the ow of new data, which makes distribution theory a particularly interesting and attractive field. Here, we provide a thorough study of the applications of the Lindley distribution and its diverse generalizations. More precisely, we review some special applications in various areas, such as time series analysis, stress strength analysis, acceptance sampling plans and data analysis. We also conduct a comparative study between the Lindley distribution and some of its generalizations by using four real-life data sets. [ABSTRACT FROM AUTHOR]

From finance and healthcare to transportation and beyond, effective time series modeling underpins a wide range of applications. While transformers have achieved success, their reliance on global context limits scalability for lengthy sequences due to the quadratic increase in computational cost with sequence length. Recent research suggests linear models can achieve comparable performance with lower complexity. However, the heterogeneity and non-stationary characteristics of time series data continue to challenge single models' ability to capture complex temporal dynamics, especially in long-term forecasting. This paper proposes MixMamba, a novel framework for time series modeling applicable across diverse domains. The framework leverages the content-based reasoning strengths of the Mamba model by integrating it as an expert within a mixture-of-experts (MoE) framework. This framework decomposes modeling into a pool of specialized experts, enabling the model to learn robust representations and capture the full spectrum of patterns present in time series data. Furthermore, a dynamic gating network is introduced within the framework. This network adaptively allocates each data segment to the most suitable expert based on its characteristics. This is crucial in non-stationary time series, as it allows the model to adjust dynamically to temporal changes in the underlying data distribution. To prevent bias towards a limited subset of experts, a load balancing loss function is incorporated. Extensive experiments on benchmark datasets demonstrate the effectiveness and robustness of our proposed method in various time series modeling tasks, including long-term and short-term forecasting, as well as classification. • Collaborative learning with adaptive expertise for efficient time series modeling. • Captures diverse patterns and long-range dependencies through specialized experts. • Dynamic gating network enhances flexibility for handling data heterogeneity. • Scales to long-term trend learning in non-stationary data with linear-time efficiency. • Outperforms single models in both forecasting and classification tasks. [ABSTRACT FROM AUTHOR]