ARIMA Research Articles

Influence of climate change on water quality in Terra Nova River basin, Pernambuco, Brazil

Climate changes tend to intensify water scarcity in arid and semi-arid regions, making water management in these areas more challenging, directly influencing hydrological dynamics, causing impacts on ecosystems and society. The Terra Nova River basin is crucial for the water security of the semi-arid region of Pernambuco, especially after the construction of the São Francisco River Integration Project (PISF), which saw the construction of six reservoirs to supply water for human consumption, agricultural, and industrial activities. Therefore, this study aims to analyzes the impacts of climate changes on water quality in the Terra Nova River basin in Pernambuco, using long-term data from the Hydrological Response Unit System for Pernambuco (SUPer). The ARIMA (Autoregressive Integrated Moving Averages) model is employed to predict water quality parameters in climate change scenarios, using data from the Intergovernmental Panel on Climate Change (IPCC) and the Oswaldo Cruz Foundation (Fiocruz). The variables include air temperature, precipitation, dissolved oxygen, nitrogen, and phosphorus. Simulated scenarios were compared with CONAMA No. 357/2005 limits, revealing potential decreases in dissolved oxygen and phosphorus concentrations, alongside an increase in nitrogen concentrations. Irregular rainfall rates, high air temperatures, and evapotranspiration, combined with conflicts by water resources, may exacerbate water access issues in the semi-arid region, worsening the water crisis and threatening water security.

Chinese Public Attitudes and Opinions on Health Policies During Public Health Emergencies: Sentiment and Topic Analysis.

By the end of 2021, the new wave of COVID-19 sparked by the Omicron variant spread rapidly due to its highly contagious nature, affecting more than 170 countries worldwide. Nucleic acid testing became the gold standard for diagnosing novel coronavirus infections. As of July 2022, numerous cities and regions in China have implemented regular nucleic acid testing policies, which have had a significant impact on socioeconomics and people's lives. This policy has garnered widespread attention on social media platforms. This study took the newly issued regular nucleic acid testing policy during the COVID-19 pandemic as an example to explore the sentiment responses and fluctuations of netizens toward new policies during public health emergencies. It aimed to propose strategies for managing public opinion on the internet and provide recommendations for policy making and public opinion control. We collected blog posts related to nucleic acid testing on Weibo from April 1, 2022, to July 31, 2022. We used the topic modeling technique latent Dirichlet allocation (LDA) to identify the most common topics posted by users. We used Bidirectional Encoder Representations from Transformers (BERT) to calculate the sentiment score of each post. We used an autoregressive integrated moving average (ARIMA) model to examine the relationship between sentiment scores and changes over time. We compared the differences in sentiment scores across various topics, as well as the changes in sentiment before and after the announcement of the nucleic acid price reduction policy (May 22) and the lifting of the lockdown policy in Shanghai (June 1). We collected a total of 463,566 Weibo posts, with an average of 3799.72 (SD 1296.06) posts published daily. The LDA topic extraction identified 8 topics, with the most numerous being the Shanghai outbreak, nucleic acid testing price, and transportation. The average sentiment score of the posts was 0.64 (SD 0.31), indicating a predominance of positive sentiment. For all topics, posts with positive sentiment consistently outnumbered those with negative sentiment (χ27=24,844.4, P<.001). The sentiment scores of posts related to "nucleic acid testing price" decreased after May 22 compared with before (t120=3.882, P<.001). Similarly, the sentiment scores of posts related to the "Shanghai outbreak" decreased after June 1 compared with before (t120=11.943, P<.001). During public health emergencies, the topics of public concern were diverse. Public sentiment toward the regular nucleic acid testing policy was generally positive, but fluctuations occurred following the announcement of key policies. To understand the primary concerns of the public, the government needs to monitor social media posts by citizens. By promptly sharing information on media platforms and engaging in effective communication, the government can bridge the information gap between the public and government agencies, fostering a positive public opinion environment.

Research on Vegetable Sales and Pricing Based on ARIMA Model and Regression Analysis

Research on Vegetable Sales and Pricing Based on ARIMA Model and Regression Analysis

Vegetable Pricing and Replenishment Strategy Based on Multivariate Polynomial Regression and SARIMA Model

Fresh products are one of the important parts of the profitability of superstores, and it is very important for merchants to make effective replenishment and pricing suggestions based on historical data. In order to give a reasonable vegetable pricing and replenishment plan, this paper processes the data and then uses multivariate polynomial regression to regress the sales volume and cost-plus pricing of the category, to obtain the relationship between the sales volume of the category and the cost-plus pricing; and combines with the SARIMA model to further give the recommended replenishment quantity; considering the impact of vegetable freshness on pricing, a freshness-based pricing model is proposed, and the optimal pricing and optimal restocking quantity formula, and proved its correctness in principle. In this study, the pricing and replenishment strategies of reference vegetables are given, and future research can further explore the market forecast of other agricultural products on this basis to broaden the application scope of this model.

A study of fresh produce pricing and replenishment strategy based on FCM cluster analysis and ARIMA-GARCH time series analysis modelling

Aiming at the shortcomings of poor robustness and weak applicability in the formulation of pricing and replenishment strategies of fresh food superstores and the limitations of ARIMA model in pricing strategies, this paper is based on maximizing superstores' revenue, and proposes to establish an FCM cluster analysis model based on the elbow rule to analyse the correlation of different single products, and concludes that the sales volume of edible mushrooms and aquatic roots and tubers have a strong correlation, and spinach and choy sum have a strong correlation. Then the total sales volume of each category was negatively correlated with cost-plus pricing through least squares fitting. Finally, based on the ARIMA-GARCH model, a daily replenishment model was established, and it was predicted that the maximum daily replenishment of foliage and flowers would reach 213.908kg on 2 July, and the minimum daily replenishment of eggplant would reach 8.415kg on 4 July in the coming week.

Temperature Forecasts in New York City Exploiting ARIMA Model

Temperature Forecasts in New York City Exploiting ARIMA Model

COMPARISON OF ARIMA AND LSTM METHODS IN PREDICTING JAKARTA SEA LEVEL

As a coastal city, Jakarta faces enormous risks from sea level rise brought on by climate change, and it is critical to create efficient plans to anticipate and minimize any potential negative effects. Predictive modeling is essential in addressing this challenge in order to anticipate and mitigate any potential negative effects of sea level rise. Therefore, research was conducted with the aim of comparing the performance of two prediction methods, namely Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM). Sea level was predicted using both techniques up to the end of 2023. Performance indicators, including Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE), were employed to assess the quality of both prediction models. The result shows that the ARIMA (1,1,4) model is more effective in predicting sea level than the LSTM. The MAE, MAPE, and RMSE values for ARIMA (1,1,4) are 7.19, 4.86%, and 10.35, respectively. In the meantime, the sea level in Jakarta is predicted to remain reasonably steady, according to the forecasted findings from both models. This study is expected to make a significant contribution to understanding and mitigating the potential impacts of sea level rise in Jakarta as a result of climate change.

Forecasting Green Sea Turtle (Chelonia mydas) Landing in Sarawak Using Grey Model

Green sea turtles, known scientifically as Chelonia mydas, prefer to nest on specific sandy beaches in Sarawak, particularly within the Sarawak Turtle Islands (STI). The number of turtles landing, among other variables (number of eggs collected, eggs incubated, and eggs hatched) is an important element in assessing the population size in Sarawak. However, modeling and predicting the number of turtles landing presents challenges due to limited data availability, resulting in less accurate forecasts for medium and long-term periods. To overcome this problem, this study presents a Grey Model (GM) approach, leveraging its capacity to effectively model systems with limited data, irregular patterns, and a lack of prior knowledge. Using data from 1949 to 2016, GM (1,1) was found to be the most suitable model for the given dataset, exhibiting the lowest Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) as compared to other statistical models such as Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM) and Exponential Smoothing. The model also suggested that the current conditions will likely increase turtle landings. This approach will find useful applications in evaluating the conservation status of the species.

Long-Term Passenger Flow Forecasting for Rail Transit Based on Complex Networks and Informer.

With the continuous growth of urbanization, passenger flow in urban rail transit systems is steadily increasing, making accurate long-term forecasting essential for optimizing operational scheduling and enhancing service quality. However, passenger flow forecasting becomes increasingly complex due to the intricate structure of rail transit networks and external factors such as seasonal variations. To address these challenges, this paper introduces an optimized Informer model for long-term forecasting that incorporates the influences of other stations based on complex network theory. Compared to the ARIMA, LSTM, and Transformer models, this optimized Informer model excels in processing large-scale complex transit data, particularly in terms of long-term forecasting accuracy and capturing network dependencies. The results demonstrate that this forecasting approach, which integrates complex network theory with the Informer model, significantly improves the accuracy and efficiency of long-term passenger flow predictions, providing robust decision support for urban rail transit planning and management.

Temporal trends of thyroid cancer in China and globally from 1990 to 2021: an analysis of the global burden of Disease Study 2021

The incidence of thyroid cancer (TC) is increasing annually worldwide, with a growing burden. This study aims to analyze temporal trends in the burden of TC by age and sex in China and globally from 1990 to 2021, including incidence, prevalence, mortality, and disability-adjusted life years (DALYs) rates, and to predict future trends. We obtained data on TC in China and globally from 1990 to 2021 from the Global Burden of Disease (GBD) database. Using the Joinpoint regression model, we calculated and analyzed the trends in age-standardized incidence rate (ASIR), age-standardized prevalence rate (ASPR), age-standardized mortality rate (ASMR), and age-standardized DALYs rate (ASDR). Specifically, we estimated the annual percent change (APC), the average annual percent change (AAPC), and the corresponding 95% confidence intervals (95% CI) for each metric. Additionally, we conducted a decomposition analysis to investigate the impact of aging, population growth, and epidemiological factors on the incidence and mortality rates of TC. Furthermore, the autoregressive integrated moving average (ARIMA) model was employed to predict the burden of TC from 2022 to 2036. From 1990 to 2021, the ASIR for TC in China increased from 1.249 (95% uncertainty interval [UI]: 1.009–1.473) per 100,000 to 2.473 (95% UI: 1.993–3.088) per 100,000. Conversely, the ASMR decreased from 0.473 (95% UI: 0.403–0.550) per 100,000 to 0.387 (95% UI: 0.307–0.472) per 100,000. Both males and females demonstrated an upward trajectory in ASIR. However, while the ASMR for females decreased, that for males showed an overall increase despite a decline in the last decade. The age of peak onset for TC ranged from 30 to 79 years, whereas the age of peak mortality was between 50 and 89 years. The analysis using the AAPC indicates that the growth in the ASIR (AAPC = 2.242, 95% CI: 2.112–2.371) and ASPR (AAPC = 2.975, 95% CI: 2.833–3.117) in China exceeds the global rate. Furthermore, the reduction in ASMR (AAPC = -0.651, 95% CI: -0.824 - -0.479) and ASDR (AAPC = -0.590, 95% CI: -0.787 - -0.392) in China is also more pronounced than globally. Decomposition analysis indicates that the increasing TC burden in China is primarily driven by population aging, whereas globally, population growth plays a more significant role. Projections based on predictive models suggest that from 2021 to 2036, the ASIR for both China and the global population is expected to continue rising, while the ASMR is anticipated to decline further. The incidence rates of TC in China and globally have shown a continuous upward trend, which is expected to persist over the next 15 years. Additionally, although the number of male TC cases is relatively lower compared to females, the overall ASMR and ASDR for males have shown an upward trend, despite a slight decline in recent years. This highlights the need to enhance prevention, diagnosis, and treatment measures, and to develop differentiated screening and treatment strategies based on age and sex.

Modelling net primary productivity using near real-time land cover data and soil moisture information

ABSTRACT The objective of this study is to develop a methodological approach to assess ecosystem functioning in terms of carbon uptake and sequestration. To achieve this, a model was developed to forecast Net Primary Productivity (NPP) and tested in a rural area, the Thrace region (NE Greece). Monthly net photosynthesis data from July 2015 to February 2024 was estimated using the MODerate Resolution Imaging Spectroradiometer (MODIS) Aqua MYD17A2H v061 dataset for the study area. This dataset was used as the dependent variable in an Autoregressive Integrated Moving Average (ARIMA) model. The predictive ability of monthly Land Cover (LC) and Soil Moisture (SM) data at various time lags was examined, and variables found to be significant predictors were introduced into the model as external predictors. The study found that crop areas and SM conditions exert the greatest influence on NPP, with areas containing trees exerting a lesser impact. Additionally, NPP was estimated for seven different LC and SM scenarios from March 2023 to February 2024.

PERAMALAN HARGA GULA PASIR MENGGUNAKAN VARIASI KALENDER REGARIMA DENGAN MOVING HOLIDAY EFFECT (PERIODE JANUARI 2018 SAMPAI DENGAN DESEMBER 2022 DI PASAR KOTA SEMARANG)

National sugar production is decreasing day by day with the pattern of people's consumption of sugar continuing to increase, thus encouraging the government to import sugar from other countries to meet food needs. This is because the import of granulated sugar causes the price of local granulated sugar to fluctuate which can rise up to 2 times the highest retail price. Eid al-Fitr is determined based on the Islamic calendar, this will cause a shift in the date each year on the Maseh calendar, the date of the celebration of Eid al-Fitr which moves from year to year is known as the "moving holiday effect". One of the calendar variation models used to eliminate the Holiday Effect Transfer and has a simple processing flow is the RegARIMA model. The RegARIMA model method is a modeling technique that combines the ARIMA model with the regression model. In the regression model, the weight matrix is used as the independent variable and the price of granulated sugar is used as the dependent variable. The weight value is obtained based on the number of days that affect Eid, which is 14 days. Based on an analysis of the price of granulated sugar in the Semarang City market for the period January 2018 to December 2022, the best model is obtained, namely SARIMA (1,0,0)12 with the smallest AIC value and the sMAPE value obtained from forecasting data for 2021 is of 19.04%, which means that the forecasting is still at a reasonable level.

Prediction of Carbon Dioxide Concentrations in Strawberry Greenhouse by Using Time Series Models

Carbon dioxide (CO2) concentrations play an important role in plant production, as they have a direct impact on both plant growth and yield. Therefore, the objectives of this study were to predict CO2 concentrations in the greenhouse by applying time series models using five datasets. To estimate the CO2 concentrations, this study was conducted over a four-month period from 1 December 2023 to 31 March 2024, in a strawberry-cultivating greenhouse. Fifteen sensors (MCH-383SD, Lutron, Taiwan) were installed inside the greenhouse to measure CO2 concentration at 1-min intervals. Finally, the dataset was transformed into intervals of 1, 5, 10, 30, and 60 min. The time-series data were analyzed using the autoregressive integrated moving average (ARIMA) and the Prophet Forecasting Model (PFM), with performance assessed through root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The evaluation indicated that the best model performance was achieved with data collected at 1-min intervals, while model performance declined with longer intervals, with the lowest performance observed at 60-min intervals. Specifically, the ARIMA model outperformed across all data collection intervals while comparing with the PFM. The ARIMA model, with data collected at 1-min intervals, achieved an R2 of 0.928, RMSE of 7.359, and MAE of 2.832. However, both ARIMA and PFM exhibited poorer performances as the interval of data collection increased, with the lowest performance at 60-min intervals where ARIMA had an R2 of 0.762, RMSE of 19.469, and MAE of 11.48. This research underscores the importance of frequent data collection for precise environmental control in greenhouse agriculture, emphasizing the critical role of short-interval data collection for accurate predictive modeling.

Application of Non-Parametric and Forecasting Models for the Sustainable Development of Energy Resources in Brazil

To achieve Sustainable Development Goal 7 (SDG7) and improve energy management efficiency, it is essential to develop models and methods to forecast and enhance the process accurately. These tools are crucial in shaping the national policymakers’ strategies and planning decisions. This study utilizes data envelopment analysis (DEA) and bootstrap computational methods to evaluate Brazil’s energy efficiency from 2004 to 2023. Additionally, it compares seasonal autoregressive integrated moving average (SARIMA) models and autoregressive integrated moving average (ARIMA) forecasting models to predict the variables’ trends for 2030. One significant contribution of this study is the development of a methodology to assess Brazil’s energy efficiency, considering environmental and economic factors to formulate results. These results can help create policies to make SDG7 a reality and advance Brazil’s energy strategies. According to the study results, the annual energy consumption rate is projected to increase by an average of 2.1% by 2030, which is accompanied by a trend of GDP growth. By utilizing existing technologies in the country, it is possible to reduce electricity consumption costs by an average of 30.58% while still maintaining the same GDP value. This demonstrates that sustainable development and adopting alternatives to minimize the increase in energy consumption can substantially impact Brazil’s energy sector, improving process efficiency and the profitability of the Brazilian industry.

Forecasting Hotel Occupancy Rates in Bali Province using the SARIMAX Method with Tourist Data as an Exogenous Variable

Tourism is a crucial economic sector in Bali, Indonesia. Sustainable tourism management requires an understanding of the dynamics between tourist numbers and hotel occupancy levels. This study uses the SARIMAX (Seasonal Autoregressive Integrated Moving Average) method to estimate between the two indicators and reveals a positive correlation between the two indicators. The SARIMAX model effectively captures seasonal patterns and external factors, providing accurate forecasts and supporting tourism management in Bali. Monthly data from 2010 to 2023 were analyzed. Accurate estimates can help tourism stakeholders in formulating appropriate management strategies to optimize the tourism sector. Implementing the right strategy can help ensure the preservation of the local environment and culture, as well as long-term economic benefits for Bali. From the data we use the SARIMAX (6,1,0) (1,1,0)12 model with an AIC value of 1920.553 and a MAPE value of 27%.

Forecasting Orange Juice Futures: LSTM, ConvLSTM, and Traditional Models Across Trading Horizons

This study evaluated the forecasting accuracy of various models over 5-day and 10-day trading horizons to predict the prices of orange juice futures (OJ = F). The analysis included traditional models like Autoregressive Integrated Moving Average (ARIMA) and advanced neural network models such as Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), Backpropagation Neural Network (BPNN), Support Vector Regression (SVR), and Convolutional Long Short-Term Memory (ConvLSTM), incorporating factors like the Commodities Index and the S&amp;P500 Index. We employed loss function metrics and various tests to assess model performance. The results indicated that for the 5-day horizon, the LSTM and ConvLSTM consistently outperformed the other models. LSTM achieved the lowest error rates and demonstrated superior capability in capturing temporal dependencies, especially in single-factor and S&amp;P500 Index predictions. ConvLSTM also performed strongly, effectively modeling spatial and temporal data patterns. In the 10-day horizon, similar trends were observed. LSTM and ConvLSTM models had significantly lower errors and better alignment with actual values. The BPNN model performed well when all factors were included, and the SVR model maintained consistent accuracy, particularly for single-factor predictions. The Diebold–Mariano (DM) test indicated significant differences in forecasting accuracy, favoring advanced neural network models. In addition, incorporating multiple influencing factors further improved predictive performance, enhancing investment outcomes and reducing risk.

Predicting China’s Energy Consumption and CO2 Emissions by Employing a Novel Grey Model

The accurate prediction of China’s energy consumption and CO2 emissions is important for the formulation of energy and environmental policies and achieving carbon neutrality. This paper proposes a new weighted error evaluation criterion that emphasizes the importance of new data, thereby enabling more accurate capture of the variation characteristics of new data and reflecting the principle of new information priority. By optimizing the development coefficient, grey action, and parameters in the time response formula of the traditional GM(1,1), a novel optimized model, OGMW(1,1), is constructed. The accuracy of the new model is verified by three cases from the literature. The future trends of primary energy, oil, and coal consumption and CO2 emissions in China are predicted over the next five years. The conclusions are as follows: First, the new weighted error evaluation criteria are effective and reasonable and can indicate whether a grey model can reliably use the most recent information for modeling. Second, based on the new error evaluation criteria, the development coefficient, ash action, and parameter C in the time response function can be optimized. The results show that the optimization method is reasonable. Third, compared with the traditional models GM, GMO, and ARIMA, the OGMW(1,1) provides better simulation and prediction accuracy, and new information can be prioritized more effectively. Fourth, the forecasting results indicate that China will increase its consumption of primary energy, oil, and coal, as well as its CO2 emissions. Notably, the growth rates of primary energy and oil consumption are high at approximately 22.7% and 25%, coal consumption will increase slightly, and CO2 emissions will increase by approximately 11%.

MODIS Evapotranspiration Forecasting Using ARIMA and ANN Approach at a Water-Stressed Irrigation Scheme in South Africa

The forecasting of evapotranspiration (ET) in some water-stressed regions remains a major challenge due to the lack of reliable and sufficient historical datasets. For efficient water balance, ET remains the major component and its proper forecasting and quantifying is of the utmost importance. This study utilises the 18-year (2001 to 2018) MODIS ET obtained from a drought-affected irrigation scheme in the Eastern Cape Province of South Africa. This study conducts a teleconnection evaluation between the satellite-derived evapotranspiration (ET) time series and other related remotely sensed parameters such as the Normalised Difference Vegetation Index (NDVI), Normalised Difference Water Index (NDWI), Normalised Difference Drought Index (NDDI), and precipitation (P). This comparative analysis was performed by adopting the Mann–Kendall (MK) test, Sequential Mann–Kendall (SQ-MK) test, and Multiple Linear Regression methods. Additionally, the ET detailed time-series analysis with the Keiskamma River streamflow (SF) and monthly volumes of the Sandile Dam, which are water supply sources close to the study area, was performed using the Wavelet Analysis, Breaks for Additive Seasonal and Trend (BFAST), Theil–Sen statistic, and Correlation statistics. The MODIS-obtained ET was then forecasted using the Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANNs) for a period of 5 years and four modelling performance evaluations such as the Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and the Pearson Correlation Coefficient (R) were used to evaluate the model performances. The results of this study proved that ET could be forecasted using these two time-series modeling tools; however, the ARIMA modelling technique achieved lesser values according to the four statistical modelling techniques employed with the RMSE for the ARIMA = 37.58, over the ANN = 44.18; the MAE for the ARIMA = 32.37, over the ANN = 35.88; the MAPE for the ARIMA = 17.26, over the ANN = 24.26; and for the R ARIMA = 0.94 with the ANN = 0.86. These results are interesting as they give hope to water managers at the irrigation scheme and equally serve as a tool to effectively manage the irrigation scheme.

Forecasting trends in food security with real time data

Early warning systems are an essential tool for effective humanitarian action. Advance warnings on impending disasters facilitate timely and targeted response which help save lives and livelihoods. In this work we present a quantitative methodology to forecast levels of food consumption for 60 consecutive days, at the sub-national level, in four countries: Mali, Nigeria, Syria, and Yemen. The methodology is built on publicly available data from the World Food Programme’s global hunger monitoring system which collects, processes, and displays daily updates on key food security metrics, conflict, weather events, and other drivers of food insecurity. In this study we assessed the performance of various models including Autoregressive Integrated Moving Average (ARIMA), Extreme Gradient Boosting (XGBoost), Long Short Term Memory (LSTM) Network, Convolutional Neural Network (CNN), and Reservoir Computing (RC), by comparing their Root Mean Squared Error (RMSE) metrics. Our findings highlight Reservoir Computing as a particularly well-suited model in the field of food security given both its notable resistance to over-fitting on limited data samples and its efficient training capabilities. The methodology we introduce establishes the groundwork for a global, data-driven early warning system designed to anticipate and detect food insecurity.

Evidence of the Long-Term Protective Effect of Moderate-Intensity Physical Activity on Cognitive Function in Middle-Aged and Elderly Individuals: A Predictive Analysis of Longitudinal Studies.

To investigate the effects of different intensities of physical activity (PA) on cognitive function in middle-aged and elderly individuals, and to predict future trends in cognitive ability using longitudinal data to assess the long-term role of PA in cognitive preservation. Data from the China Health and Retirement Longitudinal Study (CHARLS) were utilized. Mixed-effects models were employed to analyze the impacts of low-intensity PA (LPA), moderate-intensity PA (MPA), and vigorous-intensity PA (VPA) on overall cognition, episodic memory, and mental intactness. Random forest and XGBoost machine learning methods were employed to further validate the effects of PA. ARIMA models predicted future cognitive trends under the influence of PA. MPA demonstrated significant advantages in preserving cognitive function, particularly in overall cognition and episodic memory. While LPA had some protective effects, they were less significant than those of MPA, and VPA did not show advantages. Machine learning methods confirmed these findings. ARIMA model predictions indicated that the protective effects of MPA on cognitive function are likely to persist in the future. Moderate-intensity physical activity is associated with the preservation of cognitive ability in middle-aged and elderly individuals and may continue to provide this benefit in the future; however, further in-depth research is needed for confirmation.