Seasonal Autoregressive Integrated Moving Average Model Research Articles

Impact of COVID-19 on road crashes in Thailand

The main goal of this study is to investigate the impact of COVID-19 on road crashes in Thailand using time series and interrupted time series analysis. To achieve the goal, road crash data from the Department of Highway (DOH), which includes total crashes, single vehicle crashes, fatalities, fatal crashes, speeding crashes, and drunk driving crashes, was obtained to conduct Seasonal Autoregressive Integrated Moving Average (SARIMA) time series models and Interrupted Time Series (ITS) models. SARIMA models were applied to forecast the number of crashes in the absence of COVID-19 then compare them to the observed values to identify the difference. The impact of a policy change aimed at addressing the spread of COVID-19 was assessed using ITS models on a time series accident dataset. The goal was to ascertain if the intervention had a meaningful and causative impact on the outcome. The result showed that the first wave of COVID-19 caused a significant reduction in all road crash indicators instead of skyrocketing to a peak. After releasing the lockdown measures from the first wave of spreading, an increase was found in all of the crash indicators as well. However, the third wave of COVID-19, which lasted longest for nearly 7 months, also caused a decrease in the number of crashes, but not as much as the first wave of the outbreak. Moreover, the result from the interrupted time series also revealed that curfews and the closure of entertainment places are associated with a significant decrease in the number of speeding crashes and drunk driving crashes from 10 p.m. to 4 a.m., respectively. It can be observed that the COVID-19 countermeasures, such as curfews and bans on the sales of alcoholic beverages, led to a drop in the number of speeding and drunk driving crashes.

Research on E-Commerce Retail Demand Forecasting Based on SARIMA Model and K-means Clustering Algorithm

With the rapid development of e-commerce, precise demand forecasting and efficient inventory management have become essential for the success and profitability of retail businesses. This study focuses on demand forecasting for e-commerce retailers using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model and the K-means clustering algorithm. The research utilizes a dataset containing 1996 time series of sales data from various products, merchants, and warehouses, aiming to predict demand changes for the next 15 days. The study initially evaluates three models—Linear Regression (LR), Autoregressive Integrated Moving Average (ARIMA), and SARIMA—by fitting them to historical sales data to forecast future demand. The SARIMA model is identified as the most effective through rigorous evaluation using 1-mWAPE (mean weighted absolute percentage error) and RMSE (root mean square error) metrics. To enhance homogeneity within demand categories, the K-means clustering method is applied to divide products into four distinct groups, further refining the forecasting process.The paper also addresses the challenge of integrating new sequences into the dataset by leveraging clustering results to classify sequences and using cosine similarity to identify analogous historical time series. These matched sequences serve as the basis for demand prediction using the established SARIMA model. The findings highlight the robustness of the SARIMA model in capturing trends and seasonality, providing a reliable framework for e-commerce demand forecasting that can significantly impact inventory strategies and operational efficiency.

Empowering Sustainability: A Consumer-Centric Analysis Based on Advanced Electricity Consumption Predictions

This study addresses the critical challenge of accurately forecasting electricity consumption by utilizing Exponential Smoothing and Seasonal Autoregressive Integrated Moving Average (SARIMA) models. The research aims to enhance the precision of forecasting in the dynamic energy landscape and reveals promising outcomes by employing a robust methodology involving model application to a large amount of consumption data. Exponential Smoothing demonstrates accurate predictions, as evidenced by a low Sum of Squared Errors (SSE) of 0.469. SARIMA, with its seasonal ARIMA structure, outperforms Exponential Smoothing, achieving lower Mean Absolute Percentage Error (MAPE) values on both training (2.21%) and test (2.44%) datasets. This study recommends the adoption of SARIMA models, supported by lower MAPE values, to influence technology adoption and future-proof decision-making. This study highlights the societal implications of informed energy planning, including enhanced sustainability, cost savings, and improved resource allocation for communities and industries. The synthesis of model analysis, technological integration, and consumer-centric approaches marks a significant stride toward a resilient and efficient energy ecosystem. Decision-makers, stakeholders, and researchers may leverage findings for sustainable, adaptive, and consumer-centric energy planning, positioning the sector to address evolving challenges effectively and empowering consumers while maintaining energy efficiency.

A novel time-varying grey Fourier model for variable amplitude seasonal fluctuation sequences

PurposeA time-varying grey Fourier model (TVGFM(1,1,N)) is proposed for the simulation of variable amplitude seasonal fluctuation time series, as the performance of traditional grey models can't catch the time-varying trend well.Design/methodology/approachThe proposed model couples Fourier series and linear time-varying terms as the grey action, to describe the characteristics of variable amplitude and seasonality. The truncated Fourier order N is preselected from the alternative order set by Nyquist-Shannon sampling theorem and the principle of simplicity, then the optimal Fourier order is determined by hold-out method to improve the robustness of the proposed model. Initial value correction and the multiple transformation are also studied to improve the precision.FindingsThe new model has a broader applicability range as a result of the new grey action, attaining higher fitting and forecasting accuracy. The numerical experiment of a generated monthly time series indicates the proposed model can accurately fit the variable amplitude seasonal sequence, in which the mean absolute percentage error (MAPE) is only 0.01%, and the complex simulations based on Monte-Carlo method testify the validity of the proposed model. The results of monthly electricity consumption in China's primary industry, demonstrate the proposed model catches the time-varying trend and has good performances, where MAPEF and MAPET are below 5%. Moreover, the proposed TVGFM(1,1,N) model is superior to the benchmark models, grey polynomial model (GMP(1,1,N)), grey Fourier model (GFM(1,1,N)), seasonal grey model (SGM(1,1)), seasonal ARIMA model seasonal autoregressive integrated moving average model (SARIMA) and support vector regression (SVR).Originality/valueThe parameter estimates and forecasting of the new proposed TVGFM are studied, and the good fitting and forecasting accuracy of time-varying amplitude seasonal fluctuation series are testified by numerical simulations and a case study.

Comparison of SARIMA, Bagging Exponential Smoothing with STL Decomposition and Robust STL Decomposition for Forecasting Red Chili Production

Time series analysis enables the identification of trends and patterns in data, allowing for the development of forecasting models that predict future values. One effective approach for forecasting seasonal time series data is the Seasonal Autoregressive Integrated Moving Average (SARIMA) method. Bagging Exponential Smoothing with STL Decomposition (BES-STL) is an ensemble machine learning method aimed at enhancing forecasting accuracy. STL Method, which stands for Seasonal-Trend decomposition using Loess, is utilized to decompose time series data into three components, namely trend, seasonal, and remainder components. In the remainder component, the process of bootstrap aggregation (bagging) with Moving Block Bootstrapping (MBB) is used to obtain synthetic data, followed by averaging the value by month from the entire series as the forecast results. A comparative analysis was conducted using the SARIMA, BES-STL, and BES-RSTL models. The optimal model, with the lowest MAPE and RMSE, is then implemented to predict national red chili production. The results indicate that the SARIMA(1,1,1)(0,1,1)12 model has the best performance with a MAPE of 7.06 and a RMSE of 95,473. The top-performing model is utilized to forecast data from January to December 2022. Additionally, the forecasted results are compared to the actual data, resulting in a highly precise MAPE of 5.39.

Power consumption prediction for electric vehicle charging stations and forecasting income

Electric vehicles (EVs) are the future of the automobile industry, as they produce zero emissions and address environmental and health concerns caused by traditional fuel-poared vehicles. As more people shift towards EVs, the demand for power consumption forecasting is increasing to manage the charging stations effectively. Predicting power consumption can help optimize operations, prevent grid overloading, and power outages, and assist companies in estimating the number of charging stations required to meet demand. The paper uses three time series models to predict the electricity demand for charging stations, and the SARIMA (Seasonal Auto Regressive Integrated Moving Average) model outperforms the ARMA (Auto Regressive Moving Average) and ARIMA (Auto Regressive Integrated Moving Average) models, with the least RMSE (Root Mean Squared Error), MAE (Mean Absolute Error) and MAPE (Mean Absolute Percentage Error) scores in forecasting power demand and revenue. The data used for validation consists of charging activities over a four-year period from public charging outlets in Colorado, six months of charging data from ChargeMOD's public charging terminals in Kerala, India. Power usage is also forecasted based on wheels of vehicles, and finally, a plan subscription data from the same source is utilized to anticipate income, that helps companies develop pricing strategies to maximize profits while remaining competitive. Utility firms and charging networks may use accurate power consumption forecasts for a variety of purposes, such as power scheduling and determining the expected energy requirements for charging stations. Ultimately, precise power consumption forecasting can assist in the effective planning and design of EV charging infrastructure. The main aim of this study is to create a good time series model which can estimate the electric vehicle charging stations usage of power and verify if the firm has a good income along with some accuracy measures. The results show that SARIMA model plays a vital role in providing us with accurate information. According to the data and study here, four wheelers use more power than two and three wheelers. Also, DC charging facility uses more electricity than AC charging stations. These results can be used to determine the cost to operate the EVs and its subscriptions.

Web search volume as a near-real-time complementary surveillance tool of tick-borne encephalitis (TBE) in Italy

The Internet is an important gateway for accessing health-related information, and data generated through web queries have been increasingly used as a complementary source for monitoring and forecasting of infectious diseases and they may partially address the issue of underreporting. In this study, we assessed whether tick-borne encephalitis (TBE)-related Internet search volume may be useful as a complementary tool for TBE surveillance in Italy. Monthly Google Trends (GT) data for TBE-related information were extracted for the period between January 2017 and September 2022, corresponding to the available time series of TBE notifications in Italy. Time series modeling was performed by applying seasonal autoregressive integrated moving average (SARIMA) models with or without GT data. The search terms relative to tick bites reflected best the observed temporal distribution of TBE cases, showing a correlation coefficient of 0.81 (95 % CI: 0.71–0.88). Particularly, both the reported number of TBE cases and GT searches occurred mainly during the summer. The peak of disease notifications coincided with that of Google searches in 4 of 6 years. Once calibrated, SARIMA models with or without GT data were applied to a validation set. Retrospective forecast made by the model with GT data was associated with a lower prediction error and accurately predicted the peak timing. By contrast, the traditional SARIMA model underestimated the actual number of TBE notifications by 65 %. Timeliness, easy availability, low cost and transparency make monitoring of the TBE-related Internet search queries a promising addition to the traditional methods of TBE surveillance in Italy.

Does future tuna landing stock meet the target? Forecasting tuna landing in Malaysia using seasonal ARIMA model

PurposeThis study was conducted to forecast the monthly number of tuna landings between 2023 and 2030 and determine whether the estimated number meets the government’s target.Design/methodology/approachThe ARIMA and seasonal ARIMA (SARIMA) models were employed for time series forecasting of tuna landings from the Malaysian Department of Fisheries. The best ARIMA (p, d, q) and SARIMA(p, d, q) (P, D, Q)12 model for forecasting were determined based on model identification, estimation and diagnostics.FindingsSARIMA(1, 0, 1) (1, 1, 0)12 was found to be the best model for forecasting tuna landings in Malaysia. The result showed that the fluctuation of monthly tuna landings between 2023 and 2030, however, did not achieve the target.Research limitations/implicationsThis study provides preliminary ideas and insight into whether the government’s target for fish landing stocks can be met. Impactful results may guide the government in the future as it plans to improve the insufficient supply of tuna.Practical implicationsThe outcome of this study could raise awareness among the government and industry about how to improve efficient strategies. It is to ensure the future tuna landing meets the targets, including increasing private investment, improving human capital in catch and processing, and strengthening the system and technology development in the tuna industry.Originality/valueThis paper is important to predict the trend of monthly tuna landing stock in the next eight years, from 2023 to 2030, and whether it can achieve the government’s target of 150,000 metric tonnes.

Predicting Ghana’s Daily Natural Gas Consumption Using Time Series Models

In recent years, natural gas utilisation has seen a considerable increase because, it presents an alternative energy source that is reliable, economical and environmentally friendly for consumers. In Ghana, natural gas consumption has over the years increased due to mainly the rise in industrial and residential demands. Accurate prediction of natural gas consumption will provide stakeholders with vital information needed for planning and making informed policy decisions. This paper explores the Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA) to predict Ghana's daily natural gas consumption. The data employed for the study is daily natural gas consumption in Ghana from 2020 to 2022. The results show that both ARIMA and SARIMA models can predict the consumption of natural gas in Ghana with a good degree of accuracy. The SARIMA model slightly outperforms the ARIMA model, with a Root Mean Square Error (RMSE) of 22.25 and a Mean Absolute Percentage Error (MAPE) of 6.96%, compared to an RMSE of 23.27 and a MAPE of 7.29% for the ARIMA model. The model forecast suggests a steady natural gas consumption in Ghana but with some intermittent fluctuations.

Ten daily rainfall forecasting using SSA algorithms and Seasonal ARIMA model to determine the beginning of the rainy season

Rainfall is one of the most important hydrological parameters. Based on the prediction of rainfall, several plans related to rainfall can be carried out, including predicting the start of the planting season based on information on the beginning of the rainy season and preparing cropping patterns. The purpose of this study is to predict the ten daily rainfall for 12 months from January to December of the following year and predict the start of the rainy season based on BMKG criteria. Modeling and forecasting is carried out on the ten daily regional rainfall in the catchment of Citarum-Majalaya water gauge using the Singular Spectrum Analysis (SSA) algorithm and the Seasonal ARIMA model. Forecasting model performance is measured using error values, namely mean absolute percentage error (MAPE). The results of these performance tests illustrate that the SSA algorithm and the Seasonal ARIMA model have fairly good accuracy for predicting rainfall for the next 6 months, where each has a MAPE value of 36.8 percent and 40.0 percent. Prediction of the beginning of the rainy season at the study location based on SSA forecasting results is most likely to occur on the 3rd of October, in accordance with BMKG predictions. whereas based on the results of the SARIMA forecasting model, the beginning of the rainy season is likely to occur on the 1st of November, and another possibility that the beginning of the rainy season will occur on the 2nd of October.

The prediction and analysis of global climate change based on SARIMA

Global climate change is a significant challenge that the world is currently facing. Accurate prediction of global climate change is essential for environmental protection, agricultural production, and social development. This study explores the utilization of the Seasonal Autoregressive Integrated Moving Average (SARIMA) model for forecasting global climate change. The SARIMA model is a machine learning algorithm that can effectively capture seasonal patterns and non-linear characteristics of climate data. The study initiates by performing data preprocessing tasks, which encompass data cleaning, managing missing values, and converting the data into a suitable format for analysis. The SARIMA model is then constructed, considering the seasonality and autocorrelation of the climate data. Historical climate data is used to train the SARIMA models, which are then utilized to forecast future global climate changes. The predictive performance of the models is evaluated to validate the effectiveness and accuracy of the SARIMA model in global climate change prediction. Experimental results indicate that the SARIMA model effectively captures the underlying patterns and dynamics of the climate data. The accurate predictions of the SARIMA model have practical implications for understanding and forecasting global climate change. These forecasts provide insightful information for policy formulation and decision-making, aiding in the development of innovative strategies to mitigate and adapt to climate change.

A SARIMA time series forecasting for dengue cases for reporting to Yangon Region, Myanmar

Dengue fever is a significant public health challenge in Myanmar, which requires accurate monitoring to mitigate its impact. The study aimed to develop a forecasting model for dengue cases in Myanmar's Yangon region using historical data from January 2002 to December 2022, with the objective of enhancing epidemiological surveillance and outbreak management. This retrospective observational study examines dengue cases in Yangon from January 2002 to December 2022, employing Seasonal Autoregressive Integrated Moving Average (SARIMA) models for predictive analysis. The most accurate model identified was SARIMA (2,0,1) (1,1,1) 12, with an AIC (Akaike Information Criterion) of 206.19 and MAPE (Mean Absolute Percentage Error) of 1.47%. According to the model, a peak in dengue cases was expected in July 2023, with an estimated 451 cases between January and December that year. Spatial variations in dengue incidence across Yangon's townships emphasize the need for targeted interventions. While the SARIMA model is valuable, it would also be important to consider many other risk factors like climate, migration patterns, virus characteristics, and socioecological factors to improve forecasting accuracy. These findings can aid public health policymakers in preventing and managing dengue outbreaks in Myanmar. However, additional research is needed to incorporate additional risk factors into the model to comprehensively understand dengue epidemiology and improve forecasting accuracy.

Modeling of the COVID-19 impact on air passenger traffic in the US, European countries, and China

The COVID-19 pandemic has changed many aspects of people's lives including travel since early 2020. Specifically, it has adversely affected people traveling by air and has hit the air transport industry significantly. But, how big is the COVID-19 impact? In order to answer such a question, we collected air passenger traffic data from the US, European countries, and China which accounted for over 75% of the world's total air passenger traffic. Air passenger traffic data in these three regions during the period January 2010 to December 2019 were modeled using seasonal autoregressive integrated moving average (ARIMA) models. Seasonal ARIMA models were used to predict air passenger traffic from January 2011 to December 2019 (just before the spread of COVID-19) and the accuracy of the models was evaluated. The models were then used to predict air passenger traffic from January 2020 to December 2022 for the case without COVID-19. The COVID-19 impacts on air passenger traffic were estimated by calculating the differences in predicted and actual air passenger numbers in monthly basis. Results showed that air passenger traffic was significantly recovered in the US and European countries but it encountered significant falls in 2021 and 2022 in China due to spikes in COVID-19 variant cases in many provinces and the implementation of zero-tolerance COVID-19 policy. Implications of the study are given.

Trend and Seasonality of Hip Fractures in Catalonia, Spain: Exploring the Influence of Climate

To describe the secular trend and seasonality of the incidence of hip fracture (HF) and its relationship with climatic variables during the period 2010–2019 in Catalonia in people aged ≥ 65 years. The results were analyzed by sex, age groups (65–74, 75–84, and ≥ 85), and types of fracture (extracapsular and intracapsular). Data on sex, age, type of fracture, year, and month of hospitalization of patients admitted with a diagnosis of HF between January 1, 2010 and December 31, 2019 were collected. Crude and standardized HF incidence (HFi) rates were obtained. Data on the monthly mean of climatological variables (temperature, insolation, icy days, rain, relative humidity, atmospheric pressure, and wind force) were obtained from the network of meteorological stations in Catalonia. Time series analytical statistics were used to identify trends and seasonality. Linear regression and a seasonal autoregressive integrated moving average (ARIMA) were used to analyze the relationship of each climatic parameter with fracture rates. In addition, generalized additive models were used to ascertain the best predictive model. The total number of HF episodes was 90,149 (74.1% in women and 25.9% in men). The total number of HFs increased by 6.4% between 2010 and 2019. The median age (SD) was 84.5 (7.14) and 54% of patients were ≥ 85 years of age. Extracapsular fractures were the most common (55%). The standardized incidence rates decreased from 728.1/100,000 (95% CI 738.6–769.3) to 624.5/100,000 (95% CI 648.7–677.0), which represents a decrease of 14.2% (p < 0.05). The decline was greater at older ages. There were seasonal variations, with higher incidences in autumn (27.2%) and winter (25.7%) and lower rates in summer (23.5%) and spring (23.6%). Seasonality was more pronounced in elderly people and men. In the bivariate regression analysis, high temperatures and greater insolation were negatively associated with the HF rate, while the number of icy days, rainy days, and high relative humidity were associated with a higher incidence of fractures in all age groups and sexes. In the regression analysis using the seasonal ARIMA model, only insolation had a consistently significant association with overall HFi, after adjusting by trend and other climatic parameters. While the global number of HFs grew in Catalonia due to increases in the elderly population, the standardized HF rate decreased during the years 2010–2019. There was a seasonal trend, with predominance in the cold months and correlations with climatic parameters, especially with insolation.

Forecasting seasonal influenza activity in Canada-Comparing seasonal Auto-Regressive integrated moving average and artificial neural network approaches for public health preparedness.

Public health preparedness is based on timely and accurate information. Time series forecasting using disease surveillance data is an important aspect of preparedness. This study compared two approaches of time series forecasting: seasonal auto-regressive integrated moving average (SARIMA) modelling and the artificial neural network (ANN) algorithm. The goal was to model weekly seasonal influenza activity in Canada using SARIMA and compares its predictive accuracy, based on root mean square prediction error (RMSE) and mean absolute prediction error (MAE), to that of an ANN. An initial SARIMA model was fit using automated model selection by minimizing the Akaike information criterion (AIC). Further inspection of the autocorrelation function and partial autocorrelation function led to 'manual' model improvements. ANNs were trained iteratively, using an automated process to minimize the RMSE and MAE. A total of 378, 462 cases of influenza was reported in Canada from the 2010-2011 influenza season to the end of the 2019-2020 influenza season, with an average yearly incidence risk of 20.02 per 100,000 population. Automated SARIMA modelling was the better method in terms of forecasting accuracy (per RMSE and MAE). However, the ANN correctly predicted the peak week of disease incidence while the other models did not. Both the ANN and SARIMA models have shown to be capable tools in forecasting seasonal influenza activity in Canada. It was shown that applying both in tandem is beneficial, SARIMA better forecasted overall incidence while ANN correctly predicted the peak week.

Deep Learning Model Effectiveness in Forecasting Limited-Size Aboveground Vegetation Biomass Time Series: Kenyan Grasslands Case Study

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

Research on Automated Pricing and Supplementation Decision-making of Vegetable Products

In fresh supermarkets, the shelf life of vegetable products is usually very short, so it is crucial to replenish inventory daily based on historical sales data and product line requirements. In order to strengthen decision-making and inventory management, this article first selects representative supermarket sales data and conducts appropriate preprocessing, and then adopts a series of effective analysis methods. Firstly, using Kappa consistency testing to explore the correlation between different vegetable categories can help determine which products may be correlated in sales. In addition, in order to better formulate pricing and replenishment strategies for vegetable products, this article introduces a cost-plus pricing model. When determining the markup coefficient, multiple linear regression was used to ensure that the price-setting process is reasonable and consistent with cost and demand. In order to further confirm the accuracy and feasibility of this method, the article calculated the R-squared value of multiple linear regression, which reached 0.975, providing strong validation for the effectiveness of this method. When formulating replenishment strategies, in order to obtain sales forecasts for vegetable categories within the next 7 days, this article also uses a seasonal ARIMA model, which helps supermarkets plan inventory and supply chain more effectively. These strengthening measures have jointly improved the decision-making ability of vegetable product management in supermarkets, promoted more effective data-driven strategies, ensured the freshness and quality of vegetable products, and maximized the economic benefits of supermarkets.

Trend analysis and prediction of gonorrhea in mainland China based on a hybrid time series model

BackgroundGonorrhea has long been a serious public health problem in mainland China that requires attention, modeling to describe and predict its prevalence patterns can help the government to develop more scientific interventions.MethodsTime series (TS) data of the gonorrhea incidence in China from January 2004 to August 2022 were collected, with the incidence data from September 2021 to August 2022 as the validation. The seasonal autoregressive integrated moving average (SARIMA) model, long short-term memory network (LSTM) model, and hybrid SARIMA-LSTM model were used to simulate the data respectively, the model performance were evaluated by calculating the mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE) of the training and validation sets of the models.ResultsThe Seasonal components after data decomposition showed an approximate bimodal distribution with a period of 12 months. The three models identified were SARIMA(1,1,1) (2,1,2)12, LSTM with 150 hidden units, and SARIMA-LSTM with 150 hidden units, the SARIMA-LSTM model fitted best in the training and validation sets, for the smallest MAPE, RMSE, and MPE.ConclusionsThe overall incidence trend of gonorrhea in mainland China has been on the decline since 2004, with some periods exhibiting an upward trend. The incidence of gonorrhea displays a seasonal distribution, typically peaking in July and December each year. The SARIMA model, LSTM model, and SARIMA-LSTM model can all fit the monthly incidence time series data of gonorrhea in mainland China. However, in terms of predictive performance, the SARIMA-LSTM model outperforms the SARIMA and LSTM models, with the LSTM model surpassing the SARIMA model. This suggests that the SARIMA-LSTM model can serve as a preferred tool for time series analysis, providing evidence for the government to predict trends in gonorrhea incidence. The model's predictions indicate that the incidence of gonorrhea in mainland China will remain at a high level in 2024, necessitating that policymakers implement public health measures in advance to prevent the spread of the disease.

Seasonal Autoregressive Integrated Moving Average (SARIMA) for Melon (Cucumis melo) Yield from 2011 to 2020 Based on Planting Date Period

The main goal of time series modelling is to collect and analyze past values to develop appropriate models that describe the inherent structure and characteristics of the series about the planting date of melon. The data used in this study are the melon yield based on twenty-nine days of planting date in March from the first to the last planting date of March 2011 to 2020, to evaluate the prediction performances of the models, two indices: the root mean squared error (RMSE) and mean absolute percentage error (MAPE) were used to compare the forecasting capabilities of the SARIMA models, Excel was used first for preparing as CSV file and then changed to time series data to make the dataset of yield and R version 4.0.2 (the R Development Core Team) was used to perform ETS and SARIMA models For planting date time series based on yield, seasonal autoregressive integral moving average models (SARIMA) were constructed. The Student's t-test was one of the statistical tests used to evaluate the effectiveness and validity of the SARIMA models Through the use of SARIMA models, it is feasible to create synthetic records that maintain the statistical properties of the historical record. Finally, the results can be applied to different planting dates based on yield. Based on the Bayesian Information Criteria (BIC) values and the overall highest R2 values of 0.94, the optimal SARIMA (0,1,1) model was chosen. No limitations were found. It is the first time that this mathematical method was used to analyze time series data for 10 years of planting date of melon and it could be useful for agronomists and horticulturists to choose the best model.

Forecasting the incidence of dengue fever in Malaysia: A comparative analysis of seasonal ARIMA, dynamic harmonic regression, and neural network models

Currently, no vaccines or specific treatments are available to treat or prevent the increasing incidence of dengue worldwide. Therefore, an accurate prediction model is needed to support the anti-dengue control strategy. The primary objective of this study is to develop the most accurate model to predict future dengue cases in the Malaysian environment. This study uses secondary data collected from the weekly reports of the Ministry of Health Malaysia (MOH) website over six years, from 2017 to 2022. Three forecasting techniques, including seasonal autoregressive integrated moving average (SARIMA), dynamic harmonic regression (DHR), and neural network autoregressive model (NNAR), were first fitted to the estimation part of the data. First, several SARIMA models were run, and the best seasonal model identified was SARIMA (0, 1, 2) (1, 1, 1)52. The best DHR model was obtained with a Fourier term of 2, as this corresponds to the lowest Akaike Information Criteria (AIC) value. The NNAR (9, 1, 6)52 was considered the best choice among the NNAR models due to its superior performance in terms of the lowest error measures. The comparison among the three techniques revealed that the DHR model was the best due to its lowest MAPE and RMSE values. Thus, the DHR model was used to generate future forecasts of weekly dengue cases in Malaysia until 2023. The results showed that the model predicted more than a thousand dengue cases around weeks 27 to 32. The results showed an increase in dengue cases after the end of the monsoon season, which lasted about five months. This technique is proving to be valuable for health administrators in improving preparedness.