Seasonal Autoregressive Integrated Moving Average Research Articles

Fast autoregressive tensor decomposition for online real-time traffic flow prediction

Online real-time traffic flow prediction typically offers better real-time performance than offline prediction. However, existing studies rarely discussed online real-time traffic flow prediction and the balance between prediction accuracy and computational costs. Training and predicting traffic flow data with complex patterns by artificial intelligence models is usually time-consuming. Some high-accuracy statistical learning methods also reach a performance bottleneck in terms of computational speed. Therefore, a Fast Autoregressive Tensor Decomposition (FATD) algorithm is proposed for online real-time traffic flow prediction. First, the historical tensor data is decomposed by Tucker Decomposition into factor matrices and easy-to-compute core tensors, and these core tensors are modeled by Tensor Seasonal Autoregressive Integrated Moving Average (Tensor SARIMA). Second, a future core tensor is predicted by Tensor SARIMA. By the Inverse Tucker Decomposition, the traffic flow data to be predicted is recovered. The experimental results show that the FATD algorithm can reduce the computational cost while maintaining high accuracy. Compared with baselines, the FATD algorithm reduces MAE, RMSE, and the computational cost by approximately 35.04%, 26.86%, and 99.28% on average, respectively.

Trend of the Tuberculous Pleurisy Notification Rate in Eastern China During 2017-2021: Spatiotemporal Analysis.

Tuberculous pleurisy (TP) presents a serious allergic reaction in the pleura caused by Mycobacterium tuberculosis; however, few studies have described its spatial epidemiological characteristics in eastern China. This study aimed to determine the epidemiological distribution of TP and predict its further development in Zhejiang Province. Data on all notified cases of TP in Zhejiang Province, China, from 2017 to 2021 were collected from the existing tuberculosis information management system. Analyses, including spatial autocorrelation and spatial-temporal scan analysis, were performed to identify hot spots and clusters, respectively. The prediction of TP prevalence was performed using the seasonal autoregressive integrated moving average (SARIMA), Holt-Winters exponential smoothing, and Prophet models using R (The R Foundation) and Python (Python Software Foundation). The average notification rate of TP in Zhejiang Province was 7.06 cases per 100,000 population, peaking in the summer. The male-to-female ratio was 2.18:1. In terms of geographical distribution, clusters of cases were observed in the western part of Zhejiang Province, including parts of Hangzhou, Quzhou, Jinhua, Lishui, Wenzhou, and Taizhou city. Spatial-temporal analysis identified 1 most likely cluster and 4 secondary clusters. The Holt-Winters model outperformed the SARIMA and Prophet models in predicting the trend in TP prevalence. The western region of Zhejiang Province had the highest risk of TP. Comprehensive interventions, such as chest x-ray screening and symptom screening, should be reinforced to improve early identification. Additionally, a more systematic assessment of the prevalence trend of TP should include more predictors.

Robust state-of-charge estimation method for lithium-ion batteries based on the fusion of time series relevance vector machine and filter algorithm

Data-driven methods have been widely employed in the field of Lithium-ion battery state-of-charge (SOC) estimation. However, most of these methods do not impose constraints on the rate of change between adjacent output SOC values, which leads to the issue of output SOC fluctuations when the battery current undergoes significant variations. To address this issue, a fusion algorithm is proposed in this study, which combines the multi-kernel Incremental relevance vector machine (MIRVM) and seasonal autoregressive integrated moving average (SARIMA) within an adaptive Kalman filter (AKF) framework, called SARIMA_AKF_MIRVM(SFR) algorithm. Employing the MIRVM algorithm, we aim to construct a data-driven model capturing the intricate relationship between voltage, current, and SOC of batteries under varying operating conditions. Furthermore, we shall leverage the SARIMA algorithm to establish a time series forecasting model for the adjacent SOC values. Subsequently, we shall introduce the AKF algorithm to filter prediction results associated with the MIRVM algorithm. Additionally, we shall integrate the whale optimization algorithm to obtain the optimal parameter combination for SFR algorithm. The experimental results demonstrate that the SFR algorithm exhibits exceptional generalization capabilities and robustness. The RMSE and MAE for four experimental test conditions remain below 0.3 %, with the maximum MAE reaching a mere 0.14 %.

Time series analysis on tuberculosis cases in Kazakhstan from 2014 to 2025

Abstract Tuberculosis (TB) is a major cause of morbidity and mortality worldwide. The seasonal autoregressive integrated moving average (SARIMA) model is widely used for forecasting infectious disease incidence, including TB. However, there is a lack of comprehensive research on TB epidemiology and predictions for the future in Kazakhstan. Therefore, this study adopted a time series analysis of 72 monthly observations of reported TB cases in Kazakhstan from 2014-2019. The study used monthly health data from 2014-2019 for TB cases obtained from large-scale administrative records. The methodology involved using the Augmented Dickey-Fuller test for determining stationarity, ACF and PACF for identifying model parameters, and AIC/BIC for selecting the best SARIMA model, which was evaluated based on R2 score, MAE, and MAPE. The modeling dataset comprised monthly TB notification rates from 2014 to 2018, while the forecasting dataset comprised data from 2019. The findings suggest that the SARIMA model demonstrated some efficacy in predicting TB notification rates, although there remains scope for enhancing its precision. The time series was stationary after the first difference (ADF test: t-value of -3.25, P < 0.001) and not a white noise (P < 0.01). Based on ACF and PACF graphs, a SARIMA (0,1,0) (0,1,0)12 model was deemed to be the most appropriate forecasting model, with an R2 score of 0.43, MAE of 89.73 and MAPE of 0.08. To develop effective forecasting models for TB incidence, this study examined monthly TB cases in Kazakhstan from 2014 to 2019 and analyzed the seasonal patterns of TB occurrence. It is important to note that these results are preliminary and necessitate additional validation. Future studies will include correlation analyses between TB cases and meteorological data, using more recent data for TB cases, and evaluating various TB incidence forecasting models. Key messages • A SARIMA model developed for TB in Kazakhstan showed decreasing trend using time series analysis. • Evidence-based strategies for TB prevention & control, and importance of monitoring & forecasting TB incidence highlighted in this study.

The application of Long Short-Term Memory algorithm in American multinational technology company stock prediction

The fast development in American multinational technology companies has attracted both professional and new investors to buy the stocks. However, the price of these companies are unstable and therefore hard to be predicted. The focus of this article is to use AI and deep learning algorithms to find a pattern of the stock price. Long Short-Term Memory Algorithm (LSTM) is the main algorithm used to predict the trend, and other methods including Autoregressive integrated moving average (ARIMA), Seasonal autoregressive integrated moving average (SARIMA), and prophet are also discussed in this piece.

Stochastic Approaches Systems to Predictive and Modeling Chilean Wildfires

Whether due to natural causes or human carelessness, forest fires have the power to cause devastating damage, alter the habitat of animals and endemic species, generate insecurity in the population, and even affect human settlements with significant economic losses. These natural and social disasters are very difficult to control, and despite the multidisciplinary human effort, it has not been possible to create efficient mechanisms to mitigate the effects, and they have become the nightmare of every summer season. This study focuses on forecast models for fire measurements using time-series data from the Chilean Ministry of Agriculture. Specifically, this study proposes a comprehensive methodology of deterministic and stochastic time series to forecast the fire measures required by the programs of the National Forestry Corporation (CONAF). The models used in this research are among those commonly applied for time-series data. For the number of fires series, an Autoregressive Integrated Moving Average (ARIMA) model is selected, while for the affected surface series, a Seasonal Autoregressive Integrated Moving Average (SARIMA) model is selected, in both cases due to the lowest error metrics among the models fitted. The results provide evidence on the forecast for the number of national fires and affected national surface measured by a series of hectares (ha). For the deterministic method, the best model to predict the number of fires and affected surface is double exponential smoothing with damped parameter; for the stochastic approach, the best model for forecasting the number of fires is an ARIMA (2,1,2); and for affected surface, a SARIMA(1,1,0)(2,0,1)4, forecasting results are determined both with stochastic models due to showing a better performance in terms of error metrics.

The Future of Data Storytelling for Precipitation Prediction in the Dead- Sea-Jordan Using SARIMA Model

This research presents a comprehensive study focused on precipitation prediction for the Dead Sea region utilizing the Seasonal Autoregressive Integrated Moving Average (SARIMA) model. The investigation seeks to interpret the accuracy and reliability of the SARIMA model's predictions by comparing them with predictions derived from climate modeling techniques. The evaluation is based on key performance metrics, including Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). Additionally, the paper examines the SARIMA model's predictive capabilities through a comparison with actual observations spanning the period from 2010 to 2022. The obtained results reveal an MSE of 12.84593, an MAE of 2.34407, and an RMSE of 3.584123 for this period. Significantly, the SARIMA model surpasses the predictions of prominent climate models (CMIP6), namely ACCESS_CM2, Earth3_Veg, GISS_E2, and HadGEM3, based on comparative performance assessments. The findings emphasize the robustness of the SARIMA model in capturing the essence of the observations and predicting precipitation patterns, not only through its superior performance against climate models but also through its alignment with actual observations. This study contributes to a deeper understanding of precipitation prediction in the Dead Sea region and underscores the potential of the SARIMA model in enhancing forecasting accuracy for hydrological and climatic investigations

Linear and Nonlinear Associations between Meteorological Variables and the Incidence of Malaria in Zahedan District Southwest of Iran 2000-2019

Introduction: Iran is one of the malaria-prone countries. Malaria transmission is likely to be affected by many factors, including meteorological variables. This study aimed to evaluate the effect of climate variables on malaria incidence. Methods: A secondary analysis was conducted to examine the relation between malaria and meteorological variables in Zahedan district from 2000 to 2019. We built univariate and multivariate Seasonal Autoregressive Integrated Moving Average (SARIMA) models and Generalized Additive Models (GAM)/ Generalized Additive Mixed Models (GAMM) using R software. AIC, BIC and residual tests were used to test the goodness of fit of SARIMA models, and R2 was used to select the best model in GAM/GAMM. Results: The SARIMA multivariate (1,0,1) (0,1,1)12 model, including the mean temperature and minimum humidity variables without lag, was the best fit. In nonlinear analysis, the number of malaria cases positively correlated with the month from January and peaked in May (edf=6.29). There was a generally negative correlation between malaria and time in years (edf=8.41). The mean temperature, between 20 to 30°C had the highest and slightly positive relation with the incidence of malaria (edf=7.55). Rainfall showed a negative association with small fluctuations between 20 and 45 mm and a positive association over 50 mm (edf=7.52). Mean relative humidity from above 50% had a negative relation with the number of cases (edf=6.93). The hours of sunshine in a month, until 235 hours, had a negative correlation and above 340 hours had a positive correlation with the incidence of malaria (edf=7.4). Conclusion: Meteorological variables can affect malaria occurrence.

Demand side flexibility for a Heat Booster Substation with ultra low temperature district heating

Ultra-low temperature district heating (ULTDH) systems improve the heat distribution efficiency by reducing thermal network losses and provide an easier access to Renewable Energy Sources (RES) by utilizing them as heat sources through Power-to-Heat solutions. In this study, Domestic Hot Water (DHW) preparation at a Heat Booster Substation (HBS) supplied with ULTDH is investigated for generating demand side flexibility. The main components of the HBS are a Hot Water Storage Tank (HWST) and an electric-driven heat pump. A genetic algorithm based control strategy is developed to optimally utilize the thermal energy storage capability of the HBS by intelligently scheduling the charging of the HWST based on time-varying electricity price signals. A multi-objective control strategy is designed to reduce energy costs while generating flexibility on the demand side. The components of the HBS are numerically modeled and experimentally validated to form a dynamic non-linear model of the HBS. Further, the data-driven seasonal ARIMA model, ARIMA(0,1,1)x(0,1,1)168, is developed to forecast the DHW consumption based on a statistical analysis of the historic consumption profiles over a two-year period. The forecast RMSE of the DHW prediction model is less than 0.03kgs−1 over the two-year test period, indicating that the prediction model is able to accurately forecast the DHW consumption of the residents. With the control-oriented HBS numerical model and the DHW prediction model serving as decision support tools, the developed control strategy is implemented at the HBS. Results show that in comparison to a rule-based control strategy, the developed control strategy demonstrates significant demand side flexibility while attaining energy cost savings of more than 91% and 84% at time resolutions of 15 and 30 min for the control interval respectively. With increased volatility in electrical prices due to higher penetration of RES, greater energy cost savings of more than 150% are observed.

Gender differences in estimated excess mortality during the COVID-19 pandemic in Thailand

BackgroundThere is a limited body of research specifically examining gender inequality in excess mortality and its variations across age groups and geographical locations during the COVID-19 pandemic. This study aims to fill this gap by analyzing the patterns of gender inequality in excess all-cause mortality in Thailand during the COVID-19 pandemic.MethodsData pertaining to all-cause deaths and population between January 1, 2010, and December 31, 2021, were obtained from Thailand’s Bureau of Registration Administration. A seasonal autoregressive integrated moving average (SARIMA) technique was used to estimate excess mortality during the pandemic between January 2020 to December 2021. Gender differential excess mortality was measured as the difference in age-standardized mortality rates between men and women.ResultsOur SARIMA-based estimate of all-cause mortality in Thailand during the COVID-19 pandemic amounted to 1,032,921 deaths, with COVID-19-related fatalities surpassing official figures by 1.64 times. The analysis revealed fluctuating patterns of excess and deficit in all-cause mortality rates across different phases of the pandemic, as well as among various age groups and regions. In 2020, the most pronounced gender disparity in excess all-cause mortality emerged in April, with 4.28 additional female deaths per 100,000, whereas in 2021, the peak gender gap transpired in August, with 7.52 more male deaths per 100,000. Individuals in the 80 + age group exhibited the largest gender gap for most of the observed period. Gender differences in excess mortality were uniform across regions and over the period observed. Bangkok showed the highest gender disparity during the peak of the fourth wave, with 24.18 more male deaths per 100,000.ConclusionThe findings indicate an overall presence of gender inequality in excess mortality during the COVID-19 pandemic in Thailand, observed across age groups and regions. These findings highlight the need for further attention to be paid to gender disparities in mortality and call for targeted interventions to address these disparities.

Stochastic modelling and forecasting of relative humidity and wind speed for different zones of Kerala

The variations in climatic conditions depend on seasonal changes throughout the year. Modelling and prediction of climatic conditions can help to determine the impacts of seasonal changes in climate over a specific period of time. Climate change can directly and indirectly affect agricultural, industrial, geographical and technological sectors in our society. Agriculture and the allied sector are seriously affected by changes in climate since it leads to complete destruction of cultivated crops. In this study, in order to model and forecast relative humidity and wind speed for northern, central and southern zones of Kerala, stochastic approach using SARIMA (Seasonal Autoregressive Integrated Moving Average) model was employed. The monthly weather data for the northern zone and the central zone of Kerala was taken from the location of RARS Pilicode and RARS Pattambi for a period of 39 years (1982-2020) whereas for southern zone, data was collected from the location of RARS, Vellayani for a period of 36 years (1985-2020) with the help of data access viewer. The model validation was done using MSE (mean square error), RMSE (root mean square error), MAE (mean absolute error) and RMAPE (relative mean absolute percentage error). The RMAPE values of relative humidity and wind speed in different zones of Kerala was less than 10 per cent which indicated that fitted model is showing accurate performance. The best selected SARIMA model is used in attaining anticipated values of relative humidity and wind speed for the next 5 years.

FORECASTING QUARTERLY RICE AND CORN PRODUCTION IN THE PHILIPPINES: A COMPARATIVE STUDY OF SEASONAL ARIMA AND HOLT-WINTERS MODELS

Rice and corn are essential crops for the Philippines, playing a critical role in the nation’s economy and food security. However, the agricultural sector faces challenges, including climate variability, land constraints, and the need for imports to meet growing demand. Accurate forecasting of rice and corn production is crucial for informed decision-making and resource allocation. This research applied Seasonal Autoregressive Integrated Moving Average (SARIMA) and Holt-Winters exponential smoothing models to forecast rice and corn production. The study used quarterly production data from 1987 to 2023 obtained from the Philippine Statistics Authority. The Holt-Winters model with additive seasonality outperformed the SARIMA model for both rice and corn production, achieving lower Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) values. The findings have significant implications for policymakers, agricultural stakeholders, and commodity traders, guiding them in making informed decisions regarding import requirements. The volatility of global food prices and exchange rates can significantly impact the cost of imports, putting a strain on the country’s financial resources. Accurate forecasting models are essential for ensuring food sufficiency and making informed decisions on the amount of imports required. By adopting the Holt-Winters model and continuously improving forecasting methodologies, the Philippines can enhance food sufficiency and promote rural economic growth. The study highlighted the importance of accurate forecasting models in ensuring stable and sufficient rice and corn supplies to meet the nation’s growing demands, contributing to sustainable agricultural development and food security. Continuous research in agricultural forecasting methodologies is necessary to address the challenges posed by evolving agricultural dynamics and further enhance predictive accuracy.

Numerical modelling of a hydro-pneumatic energy storage system for smoothing power fluctuations from offshore wind

Energy storage systems are imperative in tackling the regulation of electricity supply and demand mismatch to avoid curtailment of wind energy. Co-locating energy storage in the same operating region as the offshore wind farms allows for an unobtrusive environment while also steering away from rising land prices. The presented work involves an offshore Hydro-Pneumatic Energy Storage (HPES) system made up of a subsea accumulator pre-charged with compressed air. The Energy Conversion Unit (ECU) of the system consists of a megawatt-scale hydraulic pump and a turbine. This paper discusses a novel numerical model developed in Python™ for simulating the operation of the ECU pump and turbine of the offshore HPES system by using a simple moving average, based on a time window of wind data, whilst also introducing a one-step-ahead forecasting model. The results of the research are split into two parts; Firstly, the analysis and validation of the Seasonal Auto Regressive Integrated Moving Average (SARIMA) forecasting model is performed. Secondly, results on the pump and turbine performance based on the smoothened power are shown. The research found that despite the centrifugal pump limitations due to variable head operation, a smoothened power output is efficiently (> 90 %) attained in retaining the intermittent power generated.

A Hybrid Approach Based on Seasonal Autoregressive Integrated Moving Average and Neural Network Autoregressive Models to Predict Scorpion Sting Incidence in El Oued Province, Algeria, From 2005 to 2020.

This study was designed to find the best statistical approach to scorpion sting predictions. Study Design: A retrospective study. Multiple regression, seasonal autoregressive integrated moving average (SARIMA), neural network autoregressive (NNAR), and hybrid SARIMA-NNAR models were developed to predict monthly scorpion sting cases in El Oued province. The root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) were used to quantitatively compare different models. In general, 96909 scorpion stings were recorded in El Oued province from 2005-2020. The incidence rate experienced a gradual decrease until 2012 and since then slight fluctuations have been noted. Scorpion stings occurred throughout the year with peaks in September followed by July and August and troughs in December and January. Sting cases were not evenly distributed across demographic groups; the most affected age group was 15-49 years, and males were more likely to be stung. Of the reported deaths, more than half were in children 15 and younger. Scorpion's activity was conditioned by climate factors, and temperature had the highest effect. The SARIMA(2,0,2)(1,1,1)12, NNAR(1,1,2)12, and SARIMA(2,0,2)(1,1,1)12-NNAR(1,1,2)12 were selected as the best-fitting models. The RMSE, MAE, and MAPE of the SARIMA and SARIMA-NNAR models were lower than those of the NNAR model in fitting and forecasting; however, the NNAR model could produce better predictive accuracy. The NNAR model is preferred for short-term monthly scorpion sting predictions. An in-depth understanding of the epidemiologic triad of scorpionism and the development of predictive models ought to establish enlightened, informed, better-targeted, and more effective policies.

Nonlinear autoregressive models for high accuracy early prediction of Li-ion battery end-of-life

Predictions of the state-of-health (SOH) of Li-ion batteries is an important goal in the monitoring and management of electric vehicles. In recent years, a number of pure machine-learning methods have been proposed for such predictions. In this paper, we instead consider autoregression methods and embedding strategies, which are specifically tailored to time-series problems. For the first time, we comprehensively compare both linear and nonlinear approaches, including six deep learning architectures, autoregressive integrated moving average (ARIMA) models and seasonal ARIMA models. In particular, for the first time we introduce Gaussian process nonlinear autoregression (GPNAR) for SOH prediction and show that it is superior in terms of accuracy and computational costs to the other autoregressive approaches. On the basis of two different datasets, we also demonstrate that accurate early predictions of the end-of-life (based on 50% of the data) is achievable with GPNAR without the use of features, which keeps data acquisition and processing to a minimum. Finally, we show that GPNAR is capable of capturing seasonal trends such as regeneration without additional time-consuming data analyses. Comparisons to other state-of-the-art methods in the recent literature confirm the superior performance of GPNAR.

Study of Global Temperature Levels Based on the ARIMA Model

The continuous growth of global carbon dioxide content has exacerbated global warming [1]. Based on this issue, this article mainly studies global temperature levels based on the ARIMA model. This article first uses data collection and preprocessing to determine whether the global temperature increase in March 2022 has led to a greater increase than the past 10 years. Then, ARIMA and seasonal ARIMA are established to describe the past and predict the future, predict the specific time when the average temperature reaches 20℃, and test the accuracy of the model. Finally, it is concluded that seasonal time series prediction is more accurate. For this study, this article first collects and completes the required temperature data for visual analysis. And independent sample t-tests were conducted on the two rising scenarios, and the results did not lead to a greater increase. Then the chapter describes the past data through the Run chart, and uses ARIMA and seasonal ARIMA to predict the future global temperature level [2]. Afterwards, this article adopts pre analysis on the data used and uses a predictive model to calculate the specific time to reach 20℃. The study found that the average temperature could not reach 20.00℃ in 2050 or 2100, but reached 20℃ in 3062. Finally, based on the data description and the characteristics of the data itself, this article demonstrates that seasonal ARIMA is more accurate.

Traffic speed prediction using GARCH‐GRU hybrid model

AbstractTraffic speed prediction is an integral part of an intelligent transportation system (ITS) because an advanced knowledge of traffic speed can help taking proactive preventive steps to avoid impending problems and it can also help in trip planning. Traffic speed data comprises a time series that may be modelled using any statistical or machine learning technique. In most of the cases, however, the performance of such models is bottlenecked due to heteroskedasticity usually present in such datasets. ARCH/GARCH family of models are generally used to model variance in such data. This paper presents a novel technique, termed as GARCH‐GRU, based on additive decomposition that splits data into random (residual) and deterministic parts. Random part is normalized using rolling standard deviation. GARCH (1, 1) is used to predict conditional variance of the residual and the predicted variance is then used in the basic model equation along with normalized residual that mimic white noise as required by the model. The data other than residual is modelled using a GRU model. The approach is applied to two datasets corresponding to a downtown road and a motorway. For comparison, the same datasets are exposed to three classical techniques; seasonal ARIMA, CNN and GRU techniques. The results demonstrate that the GARCH‐GRU technique outperforms others for random data of downtown road but fails to handle dynamic variations present in the motorway data.

Comparing the Trends of Vector-Borne Diseases (VBDs) before and after the COVID-19 Pandemic and Their Spatial Distribution in Southern Iran.

We aimed to model and predict the changes in the trend of vector-borne diseases (VBDs) before and after the COVID-19 pandemic in a high-risk area of Iran. This case-series study was conducted in Fars province, south of Iran, between April 2016 and July 2021. All referred cases of VBDs were considered during the five years to investigate the effect of the lockdown on the epidemiological profile of these diseases. We used time-series autoregressive integrated moving average (ARIMA) and seasonal ARIMA (SARIMA) models. Pediculosis incidence trend was rising with a peak of 1,146 per 100,000 in 2018, followed by a dramatic decrease reached to the minimum amount of 157.8 per 100,000 in 2021. In contrast, malaria and scabies had a smooth decreasing trend ranging from 2.2 per 100,000 and 7.3 per 100,000 in 2016 to a minimum of 0.2 per 100,000 in 2021, respectively. Likewise, leishmaniasis had a falling trend, with a maximum rate of 82.9 per 100,000 in 2016 to the lowest rate of 9.4 per 100,000 in 2021. However, the difference between observed and expected values revealed that the consequences of the COVID-19 pandemic had increased the number of leishmaniasis cases. Tropical regions of Iran, including Fars province, are the favorite destinations for travelers. During COVID-19 outbreaks, some reasons, such as quarantine, movement restrictions, and social distancing, reduced human-vector contact and finally led to the reduction of VBDs in this area.

Design of a Framework for Demand Forecasting Using Time Series Decomposition-Based Approach

In recent years, artificial intelligence (AI) has made highly accurate demand forecasting possible. However, improving forecast accuracy does not necessarily mean reducing inventory costs or improving service levels in supply chain and inventory management, which are closely related to demand forecasting. Workers require not only high accuracy but also a basis for making decisions a based on forecasts. Autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average (SARIMA) are demand forecasting methods with high accuracy and interpretability. However, these methods cannot provide evidence for demand fluctuations such as trends and seasonality, although they exhibit an autoregressive time-series structure. In this study, a framework for demand forecasting with high accuracy and interpretability was designed using time series decomposition and ARIMA to support decision makers in demand forecasting. The Seasonal-trend decomposition using locally estimated scatterplot smoothing (STL) is used to decompose a time series into three components trend, seasonality, and residual to provide decision makers with an easily understandable basis for demand changes. In addition, the ARIMA model is used for trends and residuals to achieve highly accurate forecasts. Comparing the prediction accuracies of the proposed STL-ARIMA and SARIMA models shows that STL-ARIMA has higher interpretability than the SARIMA model and the same accuracy.

Ionospheric prediction algorithm and its application in low-latitude regions based on the physically constrained polynomial model

With sufficient consideration of the ionospheric variation subject to solar activity and geomagnetic variation, a high-precision ionospheric prediction model, i.e., a physically constrained polynomial model (PCPM), was constructed by adding the Kp index, Dst index, F10.7P, R sunspot number, and other geomagnetic and solar variation indexes. Based on the data of Continuously Operating Reference System (CORS) stations in China Southern Power Grid, the ionospheric prediction results of PCPM were compared with the widely used and well-recognized seasonal enhanced product autoregressive integrated moving average (Arima) model without additional physical parameters. The prediction performance of the two models in different prediction time spans and the influence of model products on Precise Point Positioning-Real Time Kinematic (PPP-RTK) was emphatically analyzed. The experimental results demonstrated that the accuracy of PCPM is better than that of the seasonal Arima model on the first day of prediction, the prediction accuracy of the two models decreases with the increase of prediction time span, and the lowest accuracy of the seasonal Arima model on the first three days is 1.5 TECU. The Arima model outperforms the PCPM over a long-time span; in light of the overall accuracy, PCPM is more available in five southern provinces, outperforming the seasonal Arima model; the results of the PPP-RTK experiment showed that the PCPM could not only improve the velocity of ambiguity fixing but also improve the positioning accuracy after ambiguity fixing. Compared with the seasonal Arima model, the velocity of ambiguity fixing is increased by 16.7%, and the positioning accuracy after ambiguity fixing is improved by 61.0% in the E direction and 6.9% in the U direction compared with the results without ionospheric constraints.