Multivariate time series forecasting plays a crucial role in various domains, including finance, where accurate stock price prediction supports strategic decision-making. Traditional methods such as Autoregressive Integrated Moving Average (ARIMA), Exponential Smoothing (ETS), and Vector Autoregression (VAR) often fall short when dealing with complex, non-linear data—particularly those exhibiting long-term temporal dependencies. This study evaluates deep learning approaches, namely Multilayer Perceptron (MLP), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM), using daily AAPL stock price data from January 2020 to November 2024. The results show that the MLP model with a 10-day time window achieves the best accuracy, yielding lower values in Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) compared to CNN, LSTM, and VAR. The findings suggest that MLP is particularly effective in capturing complex patterns in multivariate time series forecasting.

The transition to renewable energy is crucial in order to attain sustainable development, lower greenhouse gas emissions, and secure long-term energy security. This study examines spatial–temporal trends in electricity generation (both renewable and non-renewable) across EU-28 countries using monthly Eurostat data (2008–2025) at the NUTS0 level. Two harmonized Space–Time Cubes (STCs) were constructed for renewable and non-renewable electricity covering the fully comparable 2017–2024 interval, while 2008–2016 data were used for descriptive validation, and 2025 data were used for one-step-ahead forecasting. In this paper, the authors present a novel multi-method approach to energy transition dynamics in Europe, integrating forecasting (ESF), hot-spot detection (EHSA), and clustering (TSC) with the help of a new spatial–temporal modeling framework. The methodology is a step forward in the development of methodological literature, since it regards predictive and exploratory GIS analytics as comparative energy transition evaluation. The paper uses Exponential Smoothing Forecast (ESF) and Emerging Hot Spot Analysis (EHSA) in a GIS-based analysis to uncover the dynamics in the region and the possible production pattern. The ESF also reported strong predictive performance in the form of the mean Root Mean Square Errors (RMSE) of renewable and non-renewable electricity generation of 422.5 GWh and 438.8 GWh, respectively. Of the EU-28 countries, seasonality was statistically significant in 78.6 per cent of locations that relied on hydropower, and 35.7 per cent of locations exhibited structural outliers associated with energy-transition asymmetries. EHSA identified short-lived localized spikes in renewable electricity production in a few Western and Northern European countries: Portugal, Spain, France, Denmark, and Sweden, termed as sporadic renewable hot spots. There were no cases of persistent or increase-based hot spots in any country; therefore, renewable growth is temporally and spatially inhomogeneous in the EU-28. In the case of non-renewable sources, a hot spot was evident in France, with an intermittent hot spot in Spain and sporadic increases over time, but otherwise, there was no statistically significant activity of hot or cold spots in the rest of Europe, indicating structural stagnation in the generation of fossil-based electricity. Time Series Clustering (TSC) determined 10 temporal clusters in the generation of renewable and non-renewable electricity. All renewable clusters were statistically significantly increasing (p < 0.001), with the most substantial increase in Cluster 4 (statistic = 9.95), observed in Poland, Finland, Portugal, and the Netherlands, indicating a transregional phase acceleration of renewable electricity production in northern, western, and eastern Europe. Conversely, all non-renewable clusters showed declining trends (p < 0.001), with Cluster 5 (statistic = −8.58) showing a concerted reduction in the use of fossil-based electricity, in line with EU decarbonization policies. The results contribute to an improved understanding of the spatial dynamics of the European energy transition and its potential to support energy security, reduce fossil fuel dependency, and foster balanced regional development. These insights are crucial to harmonize policy measures with the objectives of the European Green Deal and the United Nations Sustainable Development Goals (especially Goals 7, 11, and 13).

Classical time series methods are widely employed to analyze linear time series with a limited number of observations; however, their effectiveness relies on several strict assumptions. In contrast, artificial neural networks are particularly suitable for forecasting problems due to their data-driven nature and ability to address both linear and nonlinear challenges. Furthermore, recurrent neural networks feed the output back into the network as input, utilizing this feedback mechanism to enrich the information provided to the model. This study proposes a novel recurrent hybrid intuitionistic forecasting method utilizing a modified pi–sigma neural network, principal component analysis (PCA), and simple exponential smoothing (SES). In the proposed framework, lagged time series variables and principal components derived from the membership and non-membership values of an intuitionistic fuzzy clustering method are used as inputs. A modified particle swarm optimization (PSO) algorithm is employed to train this new hybrid network. By integrating PCA, modified pi–sigma neural networks (MPS-ANNs), and SES within a recurrent hybrid structure, the model simultaneously captures linear and nonlinear dynamics, thereby enhancing forecasting accuracy and stability. The performance of the proposed model is evaluated using diverse financial and environmental datasets, including CMC-Open (I–IV), NYC water consumption, OECD freshwater use, and ROW series. Comparative results indicate that the proposed method achieves superior accuracy and stability compared to other fuzzy-based approaches.

Background Red blood cells (RBCs) infusion is very important for the treatment of hematology patients, but how to maintain a balanced state between the supply and demand of RBCs is still a major challenge. Objective This study aimed to explore the feasibility of seasonal autoregressive integrated moving average (SARIMA) model and exponential smoothing (ES) model in predicting the clinical demand of RBCs for hematology patients each month. Methods Our study collected the monthly RBCs usage data of hematology patients from January 2014 to December 2023 to establish the SARIMA model and ES model, respectively. Then, the optimal model was used to forecast the monthly usage of RBCs from January to June 2024, and we subsequently compared the data with actual values to evaluate the prediction effect of the model. Results The best fitting SARIMA model was SARIMA (2,1,0)(1,1,1) 12 , whose R 2 = 0.603, MAE = 37.092, MAPE = 13.693, BIC = 7.896. The best fitting ES model was Winters addition model, whose R 2 = 0.702, MAE = 32.617, MAPE = 12.138, BIC = 7.485. The mean relative errors of two models were 0.085 and 0.159, respectively. The SARIMA (2,1,0)(1,1,1) 12 model performed better in prediction. Conclusion Compared with the ES model, the SARIMA model has a smaller mean relative error in predicting RBCs usage in hematology patients. DM test also verify this result. But in the future, more similar research data are needed to make research more convincing.

Recent events such as Brexit, the Russian invasion of Ukraine, and the COVID-19 pandemic have highlighted the challenges supply chains face in accurately forecasting demand during and after major disruptions. Traditional methods, which typically perform well under normal conditions, often struggle to provide reliable forecasts when demand is disrupted. As a result, many decision makers rely on their subjective judgment rather than statistical models for demand planning. However, accurate forecasting remains crucial, especially in times of disruption. To address this issue, we make two key contributions. First, using both simulated and real world data, we evaluate several traditional forecasting methods, assessing their overall performance and effectiveness across different phases of disruption. This analysis highlights their limitations in handling disrupted demand patterns. Second, we propose a shock-smoothing model, which is a modification of the single source of error state-space model underlying exponential smoothing (ETS) to include additional components that account for the disruption periods. Our findings demonstrate that the proposed model improves overall forecasting accuracy and maintains greater resilience across individual phases of disruption, positioning it as a potential valuable tool for enabling data-driven demand planning both during and after disruptions.

Exchange rate of the Indonesian Rupiah against the US Dollar is a crucial economic indicator that significantly impacts Indonesia's macroeconomic stability. Exchange rate fluctuations can affect trade, investment, inflation, and monetary policy. Therefore, an accurate forecasting method is essential to support economic decision-making. This article aims to evaluate the effectiveness of Moving Average and Exponential Smoothing methods in predicting the Rupiah exchange rate against the US Dollar. Using a qualitative approach based on literature review, this study compares findings from various previous studies on these two methods. The analysis results indicate that Exponential Smoothing is more effective in highly volatile market conditions due to its adaptability to trend changes, while Moving Average performs better in stable market conditions. These findings provide insights for academics, financial analysts, and policymakers in selecting appropriate forecasting methods to support economic stability.

BackgroundEpilepsy in Middle-aged and older adults has become a growing global health burden, with long-term alcohol consumption emerging as one of the key modifiable risk factors.MethodsLeveraging comprehensive data from the Global Burden of Disease (GBD) 2021 study, we systematically evaluated mortality and morbidity metrics - including deaths, disability-adjusted life years (DALYs), years lived with disability (YLDs), years of life lost (YLLs), and corresponding age-standardized rates (ASRs) - across 204 countries and territories. Temporal trends from 1990 to 2021 were quantified using estimated annual percentage changes (EAPCs). For burden projections through 2050, we employed three complementary modeling approaches: Bayesian age-period-cohort (BAPC) analysis, autoregressive integrated moving average (ARIMA) modeling, and Exponential Smoothing (ES) techniques. Furthermore, we conducted decomposition analyses to differentiate the impacts of demographic shifts, population aging, and epidemiological transitions, complemented by comprehensive inequality assessments.ResultsGlobally in 2021, alcohol-related epilepsy accounted for 3,003 deaths (95% uncertainty interval [UI]: 2,116-3,933) and 241,378 disability-adjusted life years (DALYs; 95% UI: 157,104–345,001) among adults aged ≥ 40 years. The disease burden demonstrated significant gender disparities, with males bearing 2.1-fold higher age-standardized DALY rates (17.09 per 100,000 population) compared to females. Geographic analysis revealed distinct patterns: middle Socio-demographic Index (SDI) quintiles showed the highest absolute case numbers, while age-standardized rates exhibited a characteristic U-shaped distribution across the SDI spectrum. Temporal trends from 1990 to 2021 showed a 67.4% increase in mortality and 64.7% rise in DALYs, contrasting with a 14.5% decline in age-standardized rates (from 19.99 to 17.09 per 100,000).ConclusionAlcohol-related epilepsy represents a growing and substantial public health burden among aging populations worldwide, demonstrating pronounced disparities by gender, age, and geographic region. Our findings highlight the critical need for a dual intervention approach: implementing targeted alcohol control measures while developing tailored regional strategies to address demographic shifts and epidemiological risks. These results provide compelling evidence for prioritizing alcohol regulation within public health initiatives for aging societies.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12889-025-25275-7.

This article introduces autoregressive (AR) linear models to psychology students and researchers through a step-by-step approach using SPSS and R. Despite their relevance, AR models remain underutilized in behavioral sciences, possibly due to conceptual challenges and difficulties interpreting autocorrelation and seasonality. Our aim is to simplify their implementation by presenting time series models as special cases of linear regression, using accessible language and practical examples. The article illustrates AR estimation using real data, incorporating lagged values as predictors of the dependent variable. Residual diagnostics, a frequently overlooked aspect in applied research, receive special attention, including figures and statistical tests. As Kmenta (1971) demonstrated, serially correlated residuals can lead to artificially low p values for the parameter estimates, potentially resulting in explanatory variables being deemed significant when they truly are not. To promote understanding, we offer intuitive visualizations and clear decision rules for model building, lag selection, and seasonality detection. We compare polynomial and AR models using the confounding test. The data set and annotated R and SPSS scripts are included to support replication and help readers learn basic syntax. We also discuss conceptual and practical limitations of moving average, integration (I), and exponential smoothing models, emphasizing the practical advantages of AR-only models in psychological contexts. Throughout, we stress the importance of aligning statistical models with theoretical assumptions and the temporal structure of data. By combining step-by-step explanations, visual guidance, and real-data applications, this tutorial provides a practical foundation for incorporating AR models into applied psychological research. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Household saving rate is an indispensable reference for people to grasp household economic behavior and analyze macroeconomic trends, and has a significant impact on capital accumulation, consumption and policy design. Taking UK household saving rate as the research object, this study compares the forecasting ability of Autoregressive Integrated Moving Average (ARIMA) model and Exponential Smoothing (ETS) model systematically based on the quarterly data from 2007Q1 to 2025Q1. Due to the economic fluctuations shown in UK from 2007Q1 to 2025Q1, it is particularly valuable to identify a robust model to forecast it. The empirical results show that the ETS (M, N, M) model is more robust than ARIMA (1,0,0) model in terms of root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) in out-of-sample forecasting. The in-sample fit of ARIMA model is better, but it may be over-fitted. This study can provide a reliable basis for the model selection of forecasting UK household saving rate, and the methodological approach and research conclusions of this study are also available for other similar countries.

As the scale and complexity of fiber-optic networks increase, the automatic detection and localization of soft failures have become crucial tasks for maintaining network services and scheduling repair actions. Fortunately, in software-defined optical networks (SDONs), the availability of extensive monitoring data can facilitate the application of machine learning (ML) techniques for effective failure management. However, the detection and localization of erbium-doped fiber amplifier (EDFA)-related failures, such as EDFA gain degradation, are hard to realize without the ubiquitous deployment of additional optical performance monitoring (OPM) devices. To address this issue, we propose an ML-based hierarchical framework for proactive detection and localization of inline EDFA gain degradation in optical networks. The framework is embedded within standard digital coherent receivers and comprises three stages, namely, time series prediction, failure detection, and failure localization. In the first stage, triple exponential smoothing (TES) is used to predict future signal power time series. Next, a normalizing flow (NF)-based neural network model is developed to detect abnormalities in the predicted signal power time series. If a failure is predicted by the two above-mentioned stages, the third stage of failure localization is automatically triggered in which a multi-task adaptive classification network (MTA-CN) is used to pinpoint an EDFA facing gain degradation by analyzing the amplitude histogram of the received symbols obtained after standard digital signal processing in the receiver. The efficacy of the proposed framework is rigorously validated through extensive experiments conducted on optical links with varying numbers of spans, i.e., 3 spans, 4 spans, and 5 spans. The results demonstrate an F1 score of 0.962 for proactive failure detection, along with F1 scores of 0.958, 0.931, and 0.919 for accurately localizing the faulty EDFAs in the three respective link configurations considered.

ABSTRACT Lithium‐ion batteries are essential in modern energy systems, supplying electricity to many consumer products that require dependable operation. This study introduces a novel method for predicting the remaining useful life (RUL) and end‐of‐life (EoL) of lithium‐ion batteries (LIBs) using the long short‐term memory (LSTM) model. The process includes getting the data ready, using exponential smoothing (ES), making a sequence, using the double lightning search algorithm (DLSA) to find the best hyperparameters, and doing iterative LSTM modeling. ES is utilized to preprocess data, reduce noise, and improve input quality for the subsequent model phases, while the iterative integration of LSTM modeling and DLSA hyperparameter optimization enhances capacity estimates across the entire battery lifecycle. The model performance is assessed using basic statistical measures, indicating consistent accuracy in predicting the state of health (SOH) and RUL. The way the time series is set up lets the LSTM accurately find important time‐based relationships, even when there is much noise. Adaptive hyperparameter selection through DLSA safeguards against fluctuating degradation rates, yielding an average error of under ±0.002 ampere‐hours in capacity prediction. Systematic sampling and normalization make calculations faster without changing the quality of the data. Data from the NASA Prognostics Center of Excellence on LIB performance and degradation are used to validate the model. This predictive modeling approach shows how data smoothing, deep learning, and intelligent search algorithms can improve RUL and EoL forecasts for real‐time battery health monitoring and operational safety.

A construction cost estimation system based on case-based reasoning and exponential smoothing

Accurate short-horizon forecasting is essential for Indonesian food-service MSMEs that plan production with perishable inputs and holiday-driven demand swings. Using monthly sales from Martabak Tip Top, Tarakan (December 2023–November 2024), this study compares a three-period moving average with single exponential smoothing under a one-step-ahead out-of-sample evaluation on a common test window. Accuracy is assessed with mean absolute percentage error (primary), mean absolute error, and root mean squared error. Single exponential smoothing delivers lower error than the moving average during the test period (MAPE 8.0 per cent versus 9.2 per cent) and projects a December requirement of about 1,710 units (moving average: about 1,720). The head-to-head evidence in an emerging-market MSME setting shows that giving greater weight to recent observations provides a more reliable operational signal than equal-weight averaging when modest level shifts occur around public holidays. Practically, using single exponential smoothing as the default planning input supports tighter bills-of-materials conversion, leaner safety-stock and reorder-point settings derived from observed forecast errors, and steadier labour scheduling, thereby reducing stockouts and waste while improving working-capital efficiency. The approach is transparent and spreadsheet-ready, offering actionable guidance for operations, finance, and policy audiences concerned with MSME performance in developing-region contexts.

Background Human papillomavirus (HPV) infection represents a major public health challenge in Latin America, with limited epidemiological data available for Honduras. HPV prevalence trends across age groups must be understood to build effective preventative measures. Objective To systematically analyze HPV prevalence among Honduran women from 1990 to 2023 across different age groups, and to project future trends through 2035 using time series forecasting methods. Methods PRISMA 2020 guided our proportions systematic review and meta-analysis. PubMed, Google Scholar, Tz’ibalnaah, and SciELO searches found age-stratified HPV prevalence studies in Honduran women from 1990 to 2023. We used MedCalc v19.7.1 for meta-analysis and Holt-Winters exponential smoothing in Python v3.10.3 for prevalence estimations. Model accuracy was measured by RMSE and MAE. Results Four studies met inclusion criteria, encompassing data from 1999 to 2023. The pooled HPV prevalence was 42% (95% CI: 21.8%), with substantial heterogeneity (I 2 > 75%). Age-stratified analysis revealed highest prevalence in women aged 15-24 years (10.65% in 2023, increased from 1.26% in 1999) and declining prevalence in women >35 years. Time series projections indicated continued increasing trends for women <35 years and stabilizing or declining trends for those ≥35 years. The Holt-Winters model demonstrated optimal fit for the 35-44 age group (RMSE=0.26, MAE=0.25), but substantial prediction errors for younger age groups (RMSE=3.77 for ages 25-34) highlight the limitations of forecasting with limited temporal data points. Conclusions HPV prevalence shows divergent age-specific trends in Honduras, with increasing rates among younger women and decreasing rates in older age groups. These findings suggest differential impacts of public health interventions across age cohorts and highlight the need for enhanced vaccination coverage and sexual health education targeting adolescents and young adults. The limited number of temporal observations constrains forecast reliability, emphasizing the need for strengthened epidemiological surveillance systems.

This study aims to analyze demand forecasting for Piping Hydraulic products at PT. X using three methods: Double Exponential Smoothing, Moving Average, and Linear Regression. The primary focus of this research is to identify significant patterns from historical demand data to generate more accurate demand predictions. By doing so, the study is expected to assist the company in improving inventory planning accuracy and supporting operational efficiency. Through a quantitative approach, this study compares the three forecasting methods to determine which is most effective in reducing prediction errors. Historical demand data were analyzed to uncover relevant trends and fluctuations, serving as the basis for selecting the appropriate forecasting method. The results indicate that all three methods used were effective in minimizing forecasting errors. Furthermore, the study provides strategic recommendations to help the company develop better production planning and design more efficient inventory policies. These findings are expected to contribute positively to the management of product demand in the future. Keywords: Demand Forecasting, Double Exponential Smoothing, Linear Regression, Moving Average, Operational Efficiency

Disclosure: R. Ali: None. A. Dixit: None. T. Jain: None. A.J. Augustine: None. P.R. Patel: None. S. Iftikhar: None. B. Padamati: None. G. Palaniswamy: None. V.A. Mendpara: None. N. Koduri: None.Background: Type 2 Diabetes Mellitus (T2DM) is a growing global health challenge, affecting over 537 million adults worldwide. While lifestyle factors like poor diet and lack of exercise are well-documented contributors, the role of alcohol consumption in increasing diabetes risk is often overlooked. Chronic alcohol use can lead to insulin resistance, obesity, and liver dysfunction, all of which heighten the likelihood of developing diabetes. Despite these metabolic effects, alcohol is rarely a focal point in diabetes prevention efforts. This study examines long-term trends in alcohol-related diabetes burden, focusing on mortality rates (ASMR), Disability (DALYs), and Future projections. Methods: This study analyses Global Burden of Disease (GBD) data from 1990 to 2021, tracking diabetes cases attributed to alcohol consumption. Time-series forecasting models (ARIMA and Exponential Smoothing) are used to predict trends through 2031. A Pearson correlation analysis (r = 0.85, p < 0.001) assesses the relationship between alcohol intake and diabetes burden over time. Additionally, a Vector Auto Regression (VAR) model evaluates the influence of alcohol taxation, obesity rates, and GDP per capita on diabetes prevalence. Results: The findings indicate a consistent rise in alcohol-related diabetes burden, with prevalence increasing from 0.015% in 1990 to 2.92% in 2021. Projections suggest this trend will continue, with alcohol-attributable diabetes burden expected to reach 1.7% by 2031 if no interventions are made. The Age-Standardized Mortality Rate (ASMR) due to alcohol-related diabetes increased from 4.59 in 1990 to 7.88 per 100,000 population in 2021, marking a 71.7% rise in diabetes-related deaths linked to alcohol consumption. Similarly, Disability-Adjusted Life Years (DALYs) due to alcohol-related diabetes increased from 4.45 million in 1990 to 7.72 million in 2021, a 73.4% rise, reflecting a growing impact on disability and reduced quality of life. Economic factors also play a key role—higher alcohol taxation is associated with lower diabetes rates while rising obesity levels and economic growth contribute to an increasing diabetes burden. Conclusions: The evidence points to alcohol consumption as a significant but often underestimated factor driving type 2 diabetes rates, impacting both mortality and disability. Without effective policy measures, the alcohol-related diabetes burden is projected to grow significantly in the coming years. Strategies such as higher alcohol taxes, public awareness campaigns, and integrating alcohol reduction into diabetes prevention programs could help mitigate these risks. Future research should focus on regional variations and long-term policy impacts to develop more effective interventions for reducing alcohol-related metabolic risks.Presentation: Monday, July 14, 2025

ObjectiveEndometriosis (EMT) is a prevalent gynecological disorder characterized by chronic pain, menstrual irregularities, and infertility. This study aims to evaluate the global burden of EMT from 1990 to 2021 and to project trends up to 2050.MethodsData from the Global Burden of Disease (GBD) 2021 database were utilized to analyze mortality, incidence, prevalence, and disability-adjusted life years (DALYs). Trends were assessed using age-standardized rates (ASR) and estimated annual percentage change (EAPC). Future burdens were projected using ARIMA and exponential smoothing models.ResultsIn 2021, there were 3,447,126 new cases of EMT reported globally. The age-standardized incidence rate (ASIR) experienced a decline of 1.07% from 1990 to 2021, while the age-standardized prevalence rate (ASPR) decreased by 0.95%. The incidence of EMT peaked among women aged 20–24 years, whereas mortality rates increased with advancing age. Projections suggest that by 2050, EMT-related deaths will rise to 68 cases, and the number of disability-adjusted life years (DALYs) will increase to 2,260,948, despite ongoing declines in both ASIR and ASPR.ConclusionAlthough the incidence and prevalence rates of EMT are declining, the disease burden remains significant among women of reproductive age. The anticipated rise in mortality and disability-adjusted life years (DALYs) in the future underscores the necessity for targeted public health policies. This study provides evidence to inform global prevention strategies. Future research should investigate the effects of population aging and lifestyle changes on the burden of EMT.

The accurate forecasting of solar power generation is becoming increasingly important in the context of renewable energy integration and intelligent energy management. The variability of solar radiation, caused by changing meteorological conditions and diurnal cycles, complicates the planning and control of photovoltaic systems and may lead to imbalances in supply and demand. This study aims to identify the most effective exponential smoothing approach for real-world PV power forecasting using actual hourly generation data from a 9 MW solar power plant in the Kyiv region, Ukraine. Four exponential smoothing techniques are analysed: Classic, a Modified classic adapted to daily generation patterns, Holt’s linear trend method, and the Holt–Winters seasonal method. The models were implemented in Microsoft Excel (Microsoft 365, version 2408) using real measurement data collected over six months. Forecasts were generated one hour ahead, and optimal smoothing constants were identified via RMSE minimisation using the Solver Add-in. Substantial differences in forecasting accuracy were observed. The Classic simple exponential smoothing model performed worst, with an RMSE of 1413.58 kW and nMAE of 9.22%. Holt’s method improved trend responsiveness (RMSE = 1052.79 kW, nMAE = 5.96%), but still lacked seasonality modelling. Holt–Winters, which incorporates both trend and seasonality, achieved a strong balance (RMSE = 1031.00 kW, nMAE = 3.7%). The best performance was observed with the modified simple exponential smoothing method, which captured the daily cycle more effectively (RMSE = 166.45 kW, nMAE = 0.84%). These results pertain to a one-step-ahead evaluation on a single plant and an extended validation window; accuracy is dependent on meteorological conditions, with larger errors during rapid cloud transi. The study identifies forecasting models that combine high accuracy with structural simplicity, intuitive implementation, and minimal parameter tuning—features that make them well-suited for integration into lightweight real-time energy control systems, despite not being evaluated in terms of runtime or memory usage. The modified simple exponential smoothing model, in particular, offers a high degree of precision and interpretability, supporting its integration into operational PV forecasting tools.

BackgroundHigh body mass index (BMI) is a well-established risk factor for ovarian and uterine cancer. However, the global, regional, and national burden of these cancers attributable to high BMI remains underexplored. This study quantifies the trends and disparities in the burden of ovarian and uterine cancer due to high BMI from 1990 to 2021 using the Global Burden of Disease (GBD) 2021 dataset.MethodsWe extracted data from GBD 2021 to estimate the mortality, incidence, and disability-adjusted life years (DALYs) attributable to high BMI for ovarian and uterine cancer across different locations and time periods. We focused on the burden of ovarian and uterine cancers among women aged 20-49. Age-standardized rates (ASRs) were calculated, and temporal trends were analyzed using the estimated annual percentage change (EAPC). Regional and national disparities were assessed using sociodemographic index (SDI) classifications. Forecasts employed the exponential smoothing (ES) and autoregressive integrated moving average (ARIMA) models.ResultsGlobally, the burden of ovarian and uterine cancer attributable to high BMI increased substantially from 1990 to 2021, with variations across regions and countries. High-income and upper-middle-income regions exhibited the highest ASRs, whereas low-SDI countries showed increasing trends in recent years. The EAPC analysis indicated a growing burden in developing regions, reflecting the rising prevalence of obesity. Age-stratified analysis revealed that middle-aged and older adults bear the highest burden.ConclusionsThe global burden of ovarian and uterine cancer attributable to high BMI has increased significantly over the past three decades. Targeted interventions, including obesity prevention and cancer screening, are crucial for mitigating this burden, particularly in emerging high-risk regions. These findings underscore the need for urgent public health strategies to address obesity-related cancer risks worldwide.

Introduction. The mortality rate in the population is the most important medical and demographic indicator considered as a result of the multifactorial influence of the living environment.The aim of the study. To assess the influence of risk-modifying environmental factors on excess mortality in the adult population of industrial centers over the first year of the COVID-19 pandemic.Materials and methods. The studies were conducted in the industrial centers of Eastern Siberia. To estimate excess mortality, daily data on mortality from all causes during the first year of the pandemic and the ten previous years were used. Adaptive exponential smoothing models and autoregressive integrated moving average models were used. The information content of factors (I) is assessed using the Kullback measure.Results. During the first year of the pandemic, the informativeness of environmental factors for all causes of excess mortality is lower than for causes unrelated to COVID-19 (I = 0.11–0.23, versus I = 0.26–0.61). For excess mortality unrelated to COVID-19, the following factors are more significant: “accumulated” chronic diseases, inhalation exposure to irritants, and availability of healthcare resources.Limitations. The analysis of excess mortality of the population was carried out using the example of the first year of the pandemic in one region.Conclusion. Excess mortality can be reduced based on a systemic analysis of dependencies using a set of mathematical and statistical methods. The significance of environmental factors during the pandemic is different for all causes of excess mortality and for causes unrelated to coronavirus infection.Compliance with ethical standards. The study does not require the submission of a biomedical ethics committee opinion or other documents.Contribution: Efimova N.V. – the concept and design of the study, processing of material, writing text, editing; Grzhibovsky A.M. – the concept and design of the study, mathematical modelling, writing text, editing; Rukavishnikov V.S. – study concept and design, editing; Mylnikova I.V. – collection and processing of material, statistical processing, writing text; Kriger E.A. – statistical analysis, mathematical modelling. All authors are responsible for the integrity of all parts of the manuscript and approval of the manuscript final version.Conflict of interest. The authors declare no conflict of interest.Funding. The study had no sponsorship.Received: May 22, 2025 / Accepted: June 26, 2025 / Published: October 20, 2025