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The interplay of exchange rates among nations significantly influences both international and domestic trade, underscoring the pivotal role of the foreign exchange market (Forex) in a country's economic landscape. Forex fluctuations have a significant impact on the everyday lives of both government agencies and the public population, directly influencing a country's prosperity or misfortune. This work proposes an advanced fuzzy time series model that incorporates domain universe sub-partitioning, parameter adjustment optimization methodologies, and interval forecasting methods. We utilized this model to examine annual exchange rate patterns between the Pakistani rupee (PKR) and the US dollar (US$), comparing its forecast accuracy to that of other models. Our proposed methodology outperformed existing methodologies in terms of forecasting precision, providing stakeholders with valuable insights for making informed, data-driven business decisions that benefit both individual firms and the country overall. [ABSTRACT FROM AUTHOR]

With the increasing penetration of renewable energy sources (RES), a battery energy storage (BES) Train supply system with flexibility and high cost-effectiveness is urgently needed. In this context, the mobile battery energy storage (BES) Train, as an efficient media of wind energy transfer to the load center with a time–space network (TSN), is proposed to assist the system operations. A real-time operation strategy for this TSN is proposed considering the vehicle routing problem (VRP) of BES Train. To achieve this goal, stochastic scheduling of BES Trains integrated with network constraints along wind power uncertainty is addressed in this work. Autoregressive integrated moving average (ARIMA) models generate power scenarios. Also, consider uncertainties related to wind power and standard deviation to optimize the placement of charging/discharging BES Train station. The uncertain parameter connected to wind power for scenario generations is observed. The proposed mixed-integer linear programming (MILP) to solve the model efficiently is based on tackling the strong coupling between integer and continuous variables. Using GAMS software, the proposed model is simulated on the IEEE six-bus systems, and case studies examine the capability of the suggested approach based on operational, flexibility, and reliability criteria. [ABSTRACT FROM AUTHOR]

From June to October, 2022, we recorded the weight, the internal temperature, and the hive entrance video traffic of ten managed honey bee (Apis mellifera) colonies at a research apiary of the Carl Hayden Bee Research Center in Tucson, AZ, USA. The weight and temperature were recorded every five minutes around the clock. The 30 s videos were recorded every five minutes daily from 7:00 to 20:55. We curated the collected data into a dataset of 758,703 records (280,760–weight; 322,570–temperature; 155,373–video). A principal objective of Part I of our investigation was to use the curated dataset to investigate the discrete univariate time series forecasting of hive weight, in-hive temperature, and hive entrance traffic with shallow artificial, convolutional, and long short-term memory networks and to compare their predictive performance with traditional autoregressive integrated moving average models. We trained and tested all models with a 70/30 train/test split. We varied the intake and the predicted horizon of each model from 6 to 24 hourly means. Each artificial, convolutional, and long short-term memory network was trained for 500 epochs. We evaluated 24,840 trained models on the test data with the mean squared error. The autoregressive integrated moving average models performed on par with their machine learning counterparts, and all model types were able to predict falling, rising, and unchanging trends over all predicted horizons. We made the curated dataset public for replication. [ABSTRACT FROM AUTHOR]

Purpose: To illustrate the interest in using interrupted time series (ITS) methods, this study evaluated the impact of the UK MHRA's March 2019 Risk Minimisation Measures (RMM) on fluoroquinolone usage. Methods: Monthly and quarterly fluoroquinolone use incidence rates from 2012 to 2022 were analysed across hospital care (Barts Health NHS Trust), primary care (Clinical Practice Research Datalink (CPRD) Aurum and CPRD GOLD), and linked records from both settings (East Scotland). Rates were stratified by age (19–59 and ≥ 60 years old). Seasonality‐adjusted segmented regression and ARIMA models were employed to model quarterly and monthly rates, respectively. Results: Post‐RMM, with segmented regression, both age groups in Barts Health experienced nearly complete reductions (> 99%); CPRD Aurum saw 20.19% (19–59) and 19.29% (≥$$ \ge $$ 60) reductions; no significant changes in CPRD GOLD; East Scotland had 45.43% (19–59) and 41.47% (≥$$ \ge $$ 60) decreases. Slope analysis indicated increases for East Scotland (19–59) and both CPRD Aurum groups, but a decrease for CPRD GOLD's ≥$$ \ge $$ 60; ARIMA detected significant step changes in CPRD GOLD not identified by segmented regression and noted a significant slope increase in Barts Health's 19–59 group. Both models showed no post‐modelling autocorrelations across databases, yet Barts Health's residuals were non‐normally distributed with non‐constant variance. Conclusions: Both segmented regression and ARIMA confirmed the reduction of fluoroquinolones use after RMM across four different UK primary care and hospital databases. Model diagnostics showed good performance in eliminating residual autocorrelation for both methods. However, diagnostics for hospital databases with low incident use revealed the presence of heteroscedasticity and non‐normal white noise using both methods. [ABSTRACT FROM AUTHOR]

Abstract The interplay of exchange rates among nations significantly influences both international and domestic trade, underscoring the pivotal role of the foreign exchange market (Forex) in a country's economic landscape. Forex fluctuations have a significant impact on the everyday lives of both government agencies and the public population, directly influencing a country's prosperity or misfortune. This work proposes an advanced fuzzy time series model that incorporates domain universe sub-partitioning, parameter adjustment optimization methodologies, and interval forecasting methods. We utilized this model to examine annual exchange rate patterns between the Pakistani rupee (PKR) and the US dollar (US$), comparing its forecast accuracy to that of other models. Our proposed methodology outperformed existing methodologies in terms of forecasting precision, providing stakeholders with valuable insights for making informed, data-driven business decisions that benefit both individual firms and the country overall.

This study introduces an Enhanced Autoregressive Integrated Moving Average (E-ARIMA) model for anomaly detection in time-series data, using vibrations monitored by CA 202 accelerometers at the Kirkuk Gas Power Plant as a case study. The objective is to overcome the limitations of traditional ARIMA models in analyzing the non-linear and dynamic nature of industrial sensory data. The novel proposed methodology includes data preparation through linear interpolation to address dataset gaps, stationarity confirmation via the Augmented Dickey-Fuller Test, and ARIMA model optimization against the Akaike Information Criterion, with a specialized time-series cross-validation technique. The results show that E-ARIMA model has superior performance in anomaly detection compared to conventional Seasonal ARIMA (SARIMA) and Vector Autoregressive models. In this regard, Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) criteria were utilized for this evaluation. Finally, the most important achievement of this research is that the results highlight the enhanced predictive accuracy of the E-ARIMA model, making it a potent tool for industrial applications such as machinery health monitoring, where early detection of anomalies is crucial to prevent costly downtimes and facilitate maintenance planning. [ABSTRACT FROM AUTHOR]

The stock market traded on Borsa Istanbul is one of the most popular investment instruments today. In fact, Turkey is one of the top five countries with the highest number of transactions per capita. The development of technology has not only made it easier for investors to turn to such investment instruments but also facilitated the analysis methods. This study aims to compare the LSTM and ARIMA models, which are among the analysis methods used, to see which one provides better performance. Unlike other studies, this study was conducted using data from the years 2020-2024 and took sectoral differences into consideration. The results showed that LSTM is more effective in capturing complex and long-term dependencies. This will help investors make more informed decisions when choosing a model. [ABSTRACT FROM AUTHOR]

The aim of the study is the development of methodology for accurate estimation of electric vehicle demand; which is paramount regarding various aspects of the firms decision-making such as optimal price, production level, and corresponding amounts of capital and labor; as well as supply chain, inventory control, capital financing, and operational expenses management. The forecasting methods utilized include statistical techniques (autoregressive integrated moving average [ARIMA], and polynomial regression), machine learning (nonlinear autoregressive neural network [NAR]), deep learning (long short-term memory [LSTM]), hybrid and combination forecasting. With regard to the latter method, our study experiments with four different combining model approaches, including the introduction of an original, novel combining method with the employment of a transcendental LASSO function, which is used to form combinations of forecasts generated by the NAR, ARIMA, and polynomial regression models. The LASSO -based combining model proved superior to all other models, for the majority of forecast error statistics; where the root mean square error (RMSE) and mean absolute percentage error (MAPE) values are 4.5% and 8% respectively lower than the average level of the component model forecasts. The major implications of our empirical findings are that greater accuracy in demand forecasting can be achieved with a combining model approach, rather than reliance on any particular, singular model. Furthermore, given its superior performance, the employment of the studys LASSO -based combining model to forecast electric vehicle demand may lead to optimal firm decision-making over a range of organizational facets, which is predicated on accurate demand function estimation. [ABSTRACT FROM AUTHOR]

Recent increases in global food demand have made this research and, therefore, the prediction of agricultural commodity prices, almost imperative. The aim of this paper is to build efficient artificial intelligence methods to effectively forecast commodity prices in light of these global events. Using three separate, well-structured models, the commodity prices of eleven major agricultural commodities that have recently caused crises around the world have been predicted. In achieving its objective, this paper proposes a novel forecasting model for agricultural commodity prices using the extreme learning machine technique optimized with the genetic algorithm. In predicting the eleven commodities, the proposed model, the extreme learning machine with the genetic algorithm, outperforms the model formed by the combination of long short-term memory with the genetic algorithm and the autoregressive integrated moving average model. Despite the fluctuations and changes in agricultural commodity prices in 2022, the extreme learning machine with the genetic algorithm model described in this study successfully predicts both qualitative and quantitative behavior in such a large number of commodities and over such a long period of time for the first time. It is expected that these predictions will provide benefits for the effective management, direction and, if necessary, restructuring of agricultural policies by providing food requirements that adapt to the dynamic structure of the countries. [ABSTRACT FROM AUTHOR]

The water quality index model is a popular tool for evaluating drinking water quality. To overcome low precision and significant errors in the traditional single prediction model, a novel autoregressive integrated moving average (ARIMA)-sparrow search algorithm (SSA)-long short-term memory (LSTM) combination model is proposed to accurately predict residual chlorine, turbidity, and pH in drinking water. First, the ARIMA model is used to extract the linear part of water quality data and output the nonlinear residual. Then, the LSTM model is used to predict the residual, and the SSA is used to find the optimal hyperparameters of the LSTM model, which plays an essential role in reducing the error of the model. To prove the superiority of the model developed, the ARIMA-SSA-LSTM model is compared with SSA-LSTM, whale optimization algorithm-LSTM, PSO-LSTM, ARIMA-LSTM, ARIMA, and LSTM. The results show that the coefficient of determination (R2) of the combination model for residual chlorine, turbidity, and pH are 0.950, 0.990, and 0.998, respectively, which are greater than all comparison models. Therefore, the model is more suitable for the prediction and analysis of water quality data. HIGHLIGHTS This article presents a new combined forecasting method to predict the trend of water quality in water distribution networks.; The autoregressive integrated moving average (ARIMA)-sparrow search algorithm (SSA)-long short-term memory (LSTM) combined model overcomes the limitations of the traditional single model.; SSA can find more suitable hyperparameters for LSTM model, so as to improve the prediction accuracy of LSTM model.;

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease caused by the SFTS virus (SFTSV). Predicting the incidence of this disease in advance is crucial for policymakers to develop prevention and control strategies. In this study, we utilized historical incidence data of SFTS (2013–2020) in Shandong Province, China to establish three univariate prediction models based on two time-series forecasting algorithms Autoregressive Integrated Moving Average (ARIMA) and Prophet, as well as a special type of recurrent neural network Long Short-Term Memory (LSTM) algorithm. We then evaluated and compared the performance of these models. All three models demonstrated good predictive capabilities for SFTS cases, with the predicted results closely aligning with the actual cases. Among the models, the LSTM model exhibited the best fitting and prediction performance. It achieved the lowest values for mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). The number of SFTS cases in the subsequent 5 years in this area were also generated using this model. The LSTM model, being simple and practical, provides valuable information and data for assessing the potential risk of SFTS in advance. This information is crucial for the development of early warning systems and the formulation of effective prevention and control measures for SFTS.

Precise demand forecasting and agile pricing strategies are crucial in modern business. This study aims to enhance these strategies by evaluating the efficacy of Holt-Winters Exponential Smoothing (HWES) and Autoregressive Integrated Moving Average (ARIMA) models. The study assesses their performance in predicting demand amid unpredictable factors and develops robust forecasting algorithms using real-world data. It evaluates HWES and ARIMA in capturing demand fluctuations, considering seasonality, market trends, and cyclic patterns. A comprehensive comparative analysis is conducted under stable and unstable economic conditions. The study also focuses on a dynamic pricing model for limited sale seasons, examining lost sales patterns over time. In the context of supply chain and inventory management, efficient demand forecasting and dynamic pricing are essential for optimizing inventory levels and minimizing costs. Supply chains must adapt quickly to demand fluctuations to avoid overstocking or stockouts, which lead to revenue losses and inefficiencies. The findings reveal that ARIMA consistently outperforms HWES in minimizing lost sales, demonstrating its efficacy in demand forecasting, mitigating stockouts, and reducing revenue losses, particularly in varying economic conditions. This research significantly contributes to current knowledge by developing tailored forecasting algorithms and a dynamic pricing model, enhancing supply chain resilience and performance in uncertain business environments.

Background: It has been claimed that the advent of modern antidepressants has reduced the suicide rate. Aims: To examine the correlation between the suicide rate and the prescription of antidepressants. Method: A dynamic regression was employed to analyze a 73-month-long, monthly time series between 2010 and 2016 in Hungary. The independent variable was the Defined Daily Dose value for the number of antidepressant (AD) prescriptions filled each month. Results: The models failed to show a significant association between the prescription of antidepressants and age- and sex-specific monthly suicide rates. Conclusions: The prescription of antidepressants in Hungary has had no impact on suicide rates. [ABSTRACT FROM AUTHOR]

Background and Aim: The global inflammatory bowel disease (IBD) escalation has precipitated an increased disease burden and economic impact, particularly in Asia. This study primarily aimed to predict the future prevalence of IBD in Korea and elucidate its evolution pattern. Methods: Using a validated diagnostic algorithm, we analyzed data from the Korean National Health Insurance Service between 2004 and 2017 to identify patients with IBD. We predicted the number and prevalence of patients with IBD from 2018 to 2048 with the autoregressive integrated moving average method. A generalized linear model (GLM) was also employed to identify factors contributing to the observed trend in IBD prevalence. Results: Our prediction model validation demonstrated an acceptable error range for IBD prevalence, with a 2.45% error rate and a mean absolute difference of 2.61. We foresee a sustained average annual increase of 4.51 IBD cases per 100 000, culminating in a prevalence of 239.73 per 100 000 by 2048. The forecasted average annual percent change was 6.17% for males and 2.75% for females over the next 30 years. The GLM analysis revealed that age, gender and time significantly impact the prevalence of IBD, with notable disparities observed between genders in specific age groups for both Crohn's disease and ulcerative colitis (all interaction P < 0.05). Conclusions: Our study forecasts a notable increase in Korean IBD prevalence by 2048, particularly among males and the 20–39 age group, highlighting the need to focus on these high‐risk groups to mitigate the future disease burden. [ABSTRACT FROM AUTHOR]

Network traffic has time-varying and nonlinear characteristics, leading to relatively low accuracy of single linear and nonlinear prediction models. Therefore, a combination model prediction method of autoregressive integrated moving average (ARIMA) model and improved particle swarm optimization (IPSO) bi-directional long short-term memory (BiLSTM) is proposed. First, the ARIMA model is used for nonsmooth modeling to obtain linear variation characteristics. Then the ARIMA prediction values are subtracted from the actual data to obtain the residual series. The optimal parameters of the BiLSTM are also optimized by IPSO. Then the optimized BiLSTM is modeled on the residual series to obtain the residual variation characteristics. Finally, the ARIMA linear predictions and the IPSO optimized BiLSTM residual predictions are summed to obtain the final prediction results. The proposed combination model outperforms the comparison model, has smaller prediction errors, and can better characterize the complex features of network traffic. [ABSTRACT FROM AUTHOR]

In this study, three different univariate municipal solid waste (MSW) disposal rate forecast models (SARIMA, Holt-Winters, Prophet) were examined using different testing periods in four North American cities with different socioeconomic conditions. A review of the literature suggests that the selected models are able to handle seasonality in a time series; however, their ability to handle outliers is not well understood. The Prophet model generally outperformed the Holt-Winters model and the SARIMA model. The MAPE and R2 of the Prophet model during pre-COVID-19 were 4.3–22.2% and 0.71–0.93, respectively. All three models showed satisfactory predictive results, especially during the pre-COVID-19 testing period. COVID-19 lockdowns and the associated regulatory measures appear to have affected MSW disposal behaviors, and all the univariate models failed to fully capture the abrupt changes in waste disposal behaviors. Modeling errors were largely attributed to data noise in seasonality and the unprecedented event of COVID-19 lockdowns. Overall, the modeling errors of the Prophet model were evenly distributed, with minimum modeling biases. The Prophet model also appeared to be versatile and successfully captured MSW disposal rates from 3000 to 39,000 tons/month. The study highlights the potential benefits of the use of univariate models in waste forecast. [ABSTRACT FROM AUTHOR]

The usage of lithium-ion batteries has significantly increased by various applications in recent years due to the advantages of long lifespan, high energy density, high power density, and eco-friendly environment benefits for sustainable usage. Although it has attracted much interest on its manufacturing process from practitioner in Industry 4.0 now, academia has relatively less concern on addressing the manufacturing process in using hybrid techniques. Thus, an advanced hybrid four-stage detection model is proposed into the original process to help identify optimal real-time production performance to hold sustainable manufacturing. This proposed hybrid model creates four key detection stages corresponded to four core challenges, including autoregressive integrated moving average (ARIMA), If-Then-Else programming rule, convolutional neural networks (CNNs), and artificial neural networks (ANNs) for achieving the purpose of economizing manpower and material resources to save manufacturing cost. (1) In cyclic process stage, ARIMA makes a successful prediction of a 20-min discharging curve with a significant low 13.38% error rate. (2) If-Then-Else programming rule sets up min–max threshold range (MMTR) on both attributes of voltage and capacity to find out failure packs before final test 1 and benefit with saving manual testing time. (3) CNNs achieve 100% classification accuracy in a productive result and save average 3.5 h for each battery pack. (4) ANNs conclude an empirical result of 100% accuracy in predicting the battery pack pass or failure. Conclusively, the study makes a comparative research with various deep learning algorithms to evaluate its performance; the proposed hybrid detection model is never seen in the challengeable lines of predicting Li-ion battery pack, and thus, it has an innovative value and priority performance for benefiting the sustainable manufacturing on offering green and renewable energy. This study contributes the research rationality and practical significance. [ABSTRACT FROM AUTHOR]

The water availability concerns have been increasing due to significant impacts of land use land cover change, and climate variability. In terms of developing countries, it is one of the biggest challenges to overcome and manage sustainability in the present and future. This study aims to evaluate the change in hydrological components and simulation of sediment yield and water yield on the large-scale basin of Kotri barrage with a change in runoff due to a change in land use land cover. This study has been done on the watershed as well as the sub-watershed level to have an accurate estimation and simulation by finding the response of hydrological components toward its natural and human-induced factors using the Soil and Water Assessment tool with high-resolution geospatial-temporal inputs over the Kotri catchment. The sediment and water yield were quantified using 42 years of simulation (1981–2022) on the subbasin level, projected to land use land cover 1990, 2000, 2010, and 2022. The increase in deforestation, agriculture, and settlement areas resulted increase in sediment load in the catchment. The sub-basins 14, 11, 12, and 13, with a high elevation and slope and with less vegetation showed higher sediment load and water yield than the sub-basins with gentle slope and with high natural vegetation cover. The sub-basins 10, 4, and 1 showed high water yield availability compared to basins 2, 3, 5, 6, 7, 8, 9. This may be the result of vegetation differences. However, contained less sediment load than basins 14, 11, 12, and 13. The main objective was to quantify the significant changes affecting catchment and sub-catchment areas, to have a better understanding of the management plan regarding land use land cover. The simulated data was further projected to prediction using machine algorithms (autoregressive integrated moving average) model for precipitation prediction, and (seasonal autoregressive integrated moving average with exogenous factors) model to predict the sediment yield and water yield in the catchment to 2060. [ABSTRACT FROM AUTHOR]

Mega-constellation network traffic forecasting provides key information for routing and resource allocation, which is of great significance to the performance of satellite networks. However, due to the self-similarity and long-range dependence (LRD) of mega-constellation network traffic, traditional linear/non-linear forecasting models cannot achieve sufficient forecasting accuracy. In order to resolve this problem, a mega-constellation network traffic forecasting model based on EMD (empirical mode decomposition)-ARIMA (autoregressive integrated moving average) and IGWO (improved grey wolf optimizer) optimized BPNN (back-propagation neural network) is proposed in this paper, which makes comprehensive utilization of linear model ARIMA, non-linear model BPNN and optimization algorithm IGWO. With the enhancement of the global optimization capability of a BPNN, the proposed hybrid model can fully realize the potential of mining linear and non-linear laws of mega-constellation network traffic, hence improving the forecasting accuracy. This paper utilizes an ON/OFF model to generate historical self-similar traffic to forecast. RMSE (root mean square error), MAE (mean absolute error), R-square and MAPE (mean absolute percentage error) are adopted as evaluation indexes for the forecasting effect. Comprehensive experimental results show that the proposed method outperforms traditional constellation network traffic forecasting schemes, with several improvements in forecasting accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Long-term time series forecasting stands as a crucial research domain within the realm of automated machine learning (AutoML). At present, forecasting, whether rooted in machine learning or statistical learning, typically relies on expert input and necessitates substantial manual involvement. This manual effort spans model development, feature engineering, hyper-parameter tuning, and the intricate construction of time series models. The complexity of these tasks renders complete automation unfeasible, as they inherently demand human intervention at multiple junctures. To surmount these challenges, this article proposes leveraging Long Short-Term Memory, which is the variant of Recurrent Neural Networks, harnessing memory cells and gating mechanisms to facilitate long-term time series prediction. However, forecasting accuracy by particular neural network and traditional models can degrade significantly, when addressing long-term time-series tasks. Therefore, our research demonstrates that this innovative approach outperforms the traditional Autoregressive Integrated Moving Average (ARIMA) method in forecasting long-term univariate time series. ARIMA is a high-quality and competitive model in time series prediction, and yet it requires significant preprocessing efforts. Using multiple accuracy metrics, we have evaluated both ARIMA and proposed method on the simulated time-series data and real data in both short and long term. Furthermore, our findings indicate its superiority over alternative network architectures, including Fully Connected Neural Networks, Convolutional Neural Networks, and Nonpooling Convolutional Neural Networks. Our AutoML approach enables non-professional to attain highly accurate and effective time series forecasting, and can be widely applied to various domains, particularly in business and finance. [ABSTRACT FROM AUTHOR]

This work intends to understand the spatial variation of channel morphological adjustment over 30 years (1987–2017). This region lacks an understanding of local variation in structural setup and its relative impact on channel morphological development as well as the direct control of channel hydrology in shaping the channel morphology. Besides measuring the reach-specific morphodynamics, this study is also engaged in understanding the impact of the allogenic processes (tectonics and gradient) and their local variations along with the response of autogenic processes and its reflection on channel morphological adjustment. Reach-specific channel morphodynamics is measured through training the observed dataset using the autoregressive integrated moving average model where the root mean squared error found at the best-fit model scenario was considered as the measure of relative variations in the morphometric variables. Channel reaches under direct structural control are morphologically most unstable where the faultline-induced possible sedimentation controls the fluvio-geomorphic processes intensively. The nature of morphological adjustment varies on different surface gradients but local variations in structural setup determine its spatial nature. The temporal variations of channel morphological parameters are responsive to peak discharge strength coupled with controls of channel gradient. Moreover, channel morphological change to hydrological variation is more sensitive on the piedmont surface compared to low gradient plain. This research contributes towards a close understanding of reach-specific channel morphological behaviour which is important while predicting its future and implementation of any river management strategies. [ABSTRACT FROM AUTHOR]

The American healthcare system allocates considerable resources compared to peer-developed nations. However, outcomes significantly trail behind, particularly in life expectancy. This study addresses questions about the enduring trends in healthcare spending as a percentage of Gross Domestic Product (GDP), notable factors contributing to this concerning trend, and the timing to apply an emergency brake to curb this accelerating trajectory. Advanced machine learning algorithms, such as Random Forest and Support Vector Regression (SVR), in conjunction with traditional statistical forecasting methods, are used to forecast future patterns. The research underscores the importance of healthcare analytics in unraveling the intricacies of the healthcare system. The findings highlight the pressing need for effective policies to confront this mounting challenge.

Income inequality is a prominent contributor to health disparities in the U.S. As a leading capitalist nation, the U.S. registers the highest healthcare expenditure among developed countries yet grapples with widening income disparities. The chasm between the rich and the underprivileged has expanded significantly in recent decades, profoundly impacting American society. This study explores the nuances of income inequality, its ramifications, and potential remedies, analyzed through the Gini Coefficient. Advanced forecasting models, including AutoRegressive Integrated Moving Average and Regression Analysis, are employed to anticipate future patterns. The research highlights the value of healthcare analytics in understanding the complexities of income inequality. The findings underscore the pressing need for effective policies to address this mounting challenge.

Abstract Background Despite the significant progress over the years, Tuberculosis remains a major public health concern and a danger to global health. This study aimed to analyze the spatial and temporal characteristics of the incidence of tuberculosis and its risk factors and to predict future trends in the incidence of Tuberculosis. Methods This study used secondary data on tuberculosis incidence and tuberculosis risk factor data from 209 countries and regions worldwide between 2000 and 2021 for analysis. Specifically, this study analyses the spatial autocorrelation of Tuberculosis incidence from 2000 to 2021 by calculating Moran’s I and identified risk factors for Tuberculosis incidence by multiple stepwise linear regression analysis. We also used the Autoregressive Integrated Moving Average model to predict the trend of Tuberculosis incidence to 2030. This study used ArcGIS Pro, Geoda and R studio 4.2.2 for analysis. Results The study found the global incidence of Tuberculosis and its spatial autocorrelation trends from 2000 to 2021 showed a general downward trend, but its spatial autocorrelation trends remained significant (Moran’s I = 0.465, P

Background: Despite the significant progress over the years, Tuberculosis remains a major public health concern and a danger to global health. This study aimed to analyze the spatial and temporal characteristics of the incidence of tuberculosis and its risk factors and to predict future trends in the incidence of Tuberculosis. Methods: This study used secondary data on tuberculosis incidence and tuberculosis risk factor data from 209 countries and regions worldwide between 2000 and 2021 for analysis. Specifically, this study analyses the spatial autocorrelation of Tuberculosis incidence from 2000 to 2021 by calculating Moran's I and identified risk factors for Tuberculosis incidence by multiple stepwise linear regression analysis. We also used the Autoregressive Integrated Moving Average model to predict the trend of Tuberculosis incidence to 2030. This study used ArcGIS Pro, Geoda and R studio 4.2.2 for analysis. Results: The study found the global incidence of Tuberculosis and its spatial autocorrelation trends from 2000 to 2021 showed a general downward trend, but its spatial autocorrelation trends remained significant (Moran's I = 0.465, P < 0.001). The risk factors for Tuberculosis incidence are also geographically specific. Low literacy rate was identified as the most pervasive and profound risk factor for Tuberculosis. Conclusions: This study shows the global spatial and temporal status of Tuberculosis incidence and risk factors. Although the incidence of Tuberculosis and Moran's Index of Tuberculosis are both declining, there are still differences in Tuberculosis risk factors across countries and regions. Even though literacy rate is the leading risk factor affecting the largest number of countries and regions, there are still many countries and regions where gender (male) is the leading risk factor. In addition, at the current rate of decline in Tuberculosis incidence, the World Health Organization's goal of ending the Tuberculosis pandemic by 2030 will be difficult to achieve. Targeted preventive interventions, such as health education and regular screening of Tuberculosis-prone populations are needed if we are to achieve the goal. The results of this study will help policymakers to identify high-risk groups based on differences in TB risk factors in different areas, rationalize the allocation of healthcare resources, and provide timely health education, so as to formulate more effective Tuberculosis prevention and control policies. [ABSTRACT FROM AUTHOR]

Economic forecasting is crucial since it benefits many different parties, such as governments, businesses, investors, and the general public. This paper presents a novel methodology for forecasting business cycles using an autoregressive integrated moving average (ARIMA), a popular linear model in time series forecasting, and a neural network with weighted fuzzy membership functions (NEWFM) as a forecasting model generator. The study used a dataset that included seven components of the leading composite index, which is used to predict positive or negative trends in several economic sectors before the GDP is compiled. The preprocessed time series data comprising the leading composite index using ARIMA were used as input vectors for the NEWFM to predict comprehensive business fluctuations. The prediction capability significantly improved through the duplicated refining process of the dataset using ARIMA and NEWFM. The combined ARIMA and NEWFM techniques exceeded ARIMA in both classification and prediction, yielding an accuracy of 91.61%. [ABSTRACT FROM AUTHOR]

Turkey is a favourable country for sugar beet production due to its climate and soil composition, and it holds a significant position among the countries producing sugar beet. Therefore, in this study, an Autoregressive Integrated Moving Average (ARIMA) was used to project the sugar beet production values for Turkey over the next ten years. The most effective model structure [ARIMA (2, 1, 3)] was created for this purpose using data from 1925 to 2020. The years 2019 and 2020 were utilized as the model's validation years. When the observed and expected sugar beet production values are compared, the data indicates that the predicted values are slightly lower than the actual ones. The results also show that by 2030, sugar beet production in Turkey would reach 20.5 million tons. This research may help policymakers plan for the storage, export, or import of sugar beets. Also, by using these data, resource waste can be avoided. [ABSTRACT FROM AUTHOR]

We introduce a comprehensive analysis of several approaches used in stock price forecasting, including statistical, machine learning, and deep learning models. The advantages and limitations of these models are discussed to provide an insight into stock price forecasting. Traditional statistical methods, such as the autoregressive integrated moving average and its variants, are recognized for their efficiency, but they also have some limitations in addressing non-linear problems and providing long-term forecasts. Machine learning approaches, including algorithms such as artificial neural networks and random forests, are praised for their ability to grasp non-linear information without depending on stochastic data or economic theory. Moreover, deep learning approaches, such as convolutional neural networks and recurrent neural networks, can deal with complex patterns in stock prices. Additionally, this study further investigates hybrid models, combining various approaches to explore their strengths and counterbalance individual weaknesses, thereby enhancing predictive accuracy. By presenting a detailed review of various studies and methods, this study illuminates the direction of stock price forecasting and highlights potential approaches for further studies refining the stock price forecasting models. [ABSTRACT FROM AUTHOR]

Introduction: The current study, focusing on a significant US (United States) colorectal cancer (CRC) burden, employs machine learning for predicting future rates among young population. Methods: CDC WONDER data from 1999 to 2022 was analyzed for CRC-related mortality in patients younger than 56 years. Temporal trends in age-adjusted mortality rates (AAMRs) were assessed via Joinpoint software. Future mortality rates were forecasted using an optimal Autoregressive Integrated Moving Average (ARIMA) model. Results: From 1999 to 2022, we observed 150,908 deaths with CRC listed as the underlying cause, predominantly in males, with an upward trend in AAMR. The ARIMA model projects an increase in CRC mortality by 2035, estimating an average annual percent change (AAPC) of 1.3% overall, 1% for females, and 1.5% for males. Conclusion: Our study findings emphasize the need for more robust preventive measures to reduce future CRC mortality among younger population. These results have significant implications for public health policies, particularly for males under 56, and underscore the importance of early screening and lifestyle modifications. [ABSTRACT FROM AUTHOR]

This study aims to enhance Microbial Fuel Cells (MFCs) reliability for remote environmental monitoring, emphasizing unexplored facets of accurate energy prediction and the integration of renewable energypowered Internet of Things (IoT) devices. Following comprehensive research, design, and component procurement, an innovative and cost-effective IoT system was developed, leveraging renewable energy from MFCs. Using an Arduino UNO-WiFi, data was collected and showcased on a web page while logged in a Google Firebase database, with an Android app created for intuitive smartphone visualization. Over four months, sensor data was accumulated. An Artificial Intelligence (AI) model, employing Autoregressive Integrated Moving Average (ARIMA), precisely forecasted MFC energy production (RMSE: 0.0119 and 0.0113 for trials 1 and 2). Despite the initial energy production surge, a subsequent decline occurred due to organic matter depletion. This prototype represents an affordable and sustainable solution for cloudbased IoT environmental monitoring with AI-driven energy forecasts, embodying innovation in renewable energy applications and sustainable practices. [ABSTRACT FROM AUTHOR]

With environmental degradation and water scarcity becoming increasingly serious, it is urgent to carry out effective management of water resources. The key task of water environment monitoring is to conduct statistics and analysis of changes in water quality characteristics. Aiming to address the problem of the strong fluctuation and strong temporal correlation of water quality characteristics prediction, a new framework for water quality prediction based on variational mode decomposition–temporal convolutional networks–autoregressive integrated moving average (VMD-TCN-ARIMA) optimized by weighted swarm the whale search algorithm (WSWOA) algorithm is proposed. First, the WSWOA was proposed by introducing the two-weighted-factor perturbation strategy and the particle swarm search method based on the whale optimization algorithm (WOA), which effectively improves the convergence speed and global search capabilities. Second, to adaptively decompose the original water quality sequences, the VMD algorithm optimized by WSWOA was utilized, which can extract features and reduce noise in the original sequence. Furthermore, the TCN-ARIMA combined model is proposed for time series analysis. The combined model is introduced to assign different algorithms to the decomposed components to reduce prediction error and modeling effort. In comparison to VMD-TCN model, the experimental results have shown that on the data of water quality characteristic dissolved oxygen (DO), the proposed model's root mean square error (RMSE) and computational time is reduced by 41.05% and 26.06%, further improving the accuracy and efficiency of prediction. [ABSTRACT FROM AUTHOR]

This research examines worldwide concerns over traffic congestion, encompassing aspects such as security, parking, pollution, and congestion. It specifically emphasizes the importance of implementing appropriate traffic light timing as a means to mitigate these issues. The research utilized a dataset from Montreal and partitioned the simulated area into various zones in order to determine congestion levels for each individual zone. A range of prediction algorithms has been employed, such as Long Short-Term Memory (LSTM), Decision Tree (DT), Recurrent Neural Network (RNN), Auto-Regressive Integrated Moving Average (ARIMA), and Seasonal Auto-Regressive Integrated Moving Average (SARIMA), to predict congestion levels at each traffic light. This information was used in a mathematical formulation to minimize the average waiting time for vehicles inside the road network. Many meta-heuristics were analyzed and compared, with the introduction of an Enhanced Bat Algorithm (EBAT) suggested for addressing the traffic signal optimization problem. Three distinct scenarios are described: fixed (with a constant green timing of 40 s), dynamic (where the timing changes in real-time based on the current level of congestion), and adaptive (which involves predicting congestion ahead of time). The scenarios are studied with low and high congestion scenarios in the road network. The Enhanced Bat Algorithm (EBAT) is introduced as a solution to optimize traffic signal timing. It enhances the original Bat algorithm by incorporating adaptive parameter tuning and guided exploration techniques that are informed by predicted congestion levels. The EBAT algorithm provides a more effective treatment for congestion problems by decreasing travel time, enhancing vehicle throughput, and minimizing pollutant emissions. [ABSTRACT FROM AUTHOR]

Objectives: Length of day variation (ΔLOD) predictions play an important role in tracking and navigation of deep-space detector, precise determination of artificial satellite orbit and climate forecasting. In full consideration of the time-varying characteristics of ΔLOD, we present the application of a hybrid technique for predicting ΔLOD. Methods: The ΔLOD predictions are generated by means of the combination of singular spectrum analysis (SSA) extrapolation for the linear trend, annual and semiannual oscillations in ΔLOD based on an iterative interpolation strategy, and autoregressive integrated moving average (ARIMA) stochastic prediction of SSA remaining residuals, referred to as SSA+ARIMA. In order to evaluate the effectiveness of this approach, the ΔLOD predictions up to 365 d into the future are calculated year-by-year for the 2-year period from Jan. 1,2000 to Dec. 31, 2001 using the data covering the previous 10 years from International Earth Rotation and Reference Systems Service C04 series. The prediction results are analyzed and compared with those obtained by machine learning methods such as back propagation neural network (BPNN), general regression neural network (GRNN) and Gaussian process (GP). Results: It is shown that the accuracy of the predictions are better than that by machine learning methods in terms of the mean absolute error (MAE) of predictions, especially for medium and long-term predictions. Compared with the predictions obtained by the BPNN, GRNN and GP, the MAE of the proposed SSA+ARIMA predictions up to 30 d and 365 d in future is reduced by 39% and 61%, respectively. Conclusions: The proposed method provides a new solution for ΔLOD prediction. [ABSTRACT FROM AUTHOR]

The present-day vehicle monitoring systems retrieve real-time data and problem codes from the car using an OBD-II device and provide the user with this data in unprocessed form. A system that is easy to use is necessary to enable users to understand the status of their car on their own, thereby preventing potentially dangerous collisions. The goal of this research is to create an understandable and user-friendly web application that will allow users to keep an eye on the health of their vehicle. The study explores the idea of the Internet of Vehicles (IoV), imagining a network in which various cars can interact with one another. The research attempts to forecast future vehicle conditions based on current data and classify them as "Good", "Moderate", or "Bad" using machine learning models such as Autoregressive Integrated Moving Average and Ordinal Logistic Regression. [ABSTRACT FROM AUTHOR]

The global value chain has introduced profound changes in international trade, economic development, and technology progress as well as greenhouse gas emissions worldwide. This paper investigated the impact of the global value chain and technological innovation on greenhouse gas emissions by introducing a partially linear functional-coefficient model based on panel data of 15 industrial sectors in China from 2000 to 2020. Moreover, the greenhouse gas emission trends of China's industrial sectors from 2024 to 2035 were predicted using the autoregressive integrated moving average model. The results showed that (1) Greenhouse gas emissions were affected negatively by global value chain position and independent innovation. Nevertheless, foreign innovation had the opposite effect. (2) The results of the partially linear functional-coefficient model implied that the inhibitory effect of independent innovation on GHG emissions decreased with an improvement in the global value chain position. (3) The positive effect of foreign innovation on greenhouse gas emissions increased and then, decreased as the global value chain position improved. (4) The prediction results indicated that greenhouse gas emissions will continue on an upward trend from 2024 to 2035, while industrial carbon dioxide emissions should peak at 10.21 Gt in 2028. This carbon-peaking goal would be achieved in China's industrial sector by actively improving the global value chain position. Addressing these issues will enable China to take full advantage of the development opportunities of participating in the global value chain. [ABSTRACT FROM AUTHOR]

BACKGROUND AND OBJECTIVE: Air pollution is associated with population growth and economic advancement. Severe cardiovascular complications that require extensive medical service are aggravated by air pollutants. This study illustrates the trend and correlation of cardiovascular disease hospital admission with air pollutants in Sabah for the past 9 years (2010–2019). The additional information obtained from this study will be useful to enhance proper environmental management and reduce air pollution in the cities of Sabah.METHOD: Ecological study design was utilized with cardiovascular disease hospital admission and ambient air pollutants in Sabah retrospective data. Data were collected from four districts with established continuous air quality monitoring stations. Collected data were analysed spatially and statistically. Autoregressive integrated moving average modelling was implemented to forecast the cardiovascular disease hospital admission.FINDING: Kota Kinabalu recorded the highest hospital admissions for cardiovascular disease, followed by Sandakan, Tawau and Keningau. The cardiovascular disease hospital admission prevalence rate in Kota Kinabalu was 12.45 per 1,000 population, followed by Sandakan, Tawau and Keningau (4.54; 4.18; and 5.88 per 1,000 population) in 2019. The cardiovascular hospital admissions increased in Kota Kinabalu, Sandakan and Tawau. The nitrogen dioxide (

The COVID-19 pandemic is perceived by many to have run its course, and forecasting its progress is no longer a topic of much interest to policymakers and researchers as it once was. Nevertheless, in order to take lessons from this extraordinary two and a half years, it still makes sense to have a critical look at the vast body of literature formed thereon, and perform comprehensive analyses in retrospect. The present study is directed towards that goal. It is distinguished from others by encompassing all of the following features simultaneously: (i) time series of 10 of the most affected countries are considered; (ii) forecasting for two types of periods, namely days and weeks, are analyzed; (iii) a wide range of exponential smoothing, autoregressive integrated moving average, and neural network autoregression models are compared by means of automatic selection procedures; (iv) basic methods for benchmarking purposes as well as mathematical transformations for data adjustment are taken into account; and (v) several test and training data sizes are examined. Our experiments show that the performance of common time series forecasting methods is highly sensitive to parameter selection, bound to deteriorate dramatically as the forecasting horizon extends, and sometimes fails to be better than that of even the simplest alternatives. We contend that the reliableness of time series forecasting of COVID-19, even for a few weeks ahead, is open to debate. Policymakers must exercise extreme caution before they make their decisions utilizing a time series forecast of such pandemics.

Abstract Introduction The current study, focusing on a significant US (United States) colorectal cancer (CRC) burden, employs machine learning for predicting future rates among young population. Methods CDC WONDER data from 1999 to 2022 was analyzed for CRC‐related mortality in patients younger than 56 years. Temporal trends in age‐adjusted mortality rates (AAMRs) were assessed via Joinpoint software. Future mortality rates were forecasted using an optimal Autoregressive Integrated Moving Average (ARIMA) model. Results From 1999 to 2022, we observed 150,908 deaths with CRC listed as the underlying cause, predominantly in males, with an upward trend in AAMR. The ARIMA model projects an increase in CRC mortality by 2035, estimating an average annual percent change (AAPC) of 1.3% overall, 1% for females, and 1.5% for males. Conclusion Our study findings emphasize the need for more robust preventive measures to reduce future CRC mortality among younger population. These results have significant implications for public health policies, particularly for males under 56, and underscore the importance of early screening and lifestyle modifications.