Forecasting Rice Paddy Production in Aceh Using ARIMA and Exponential Smoothing Models

This paper investigates the application of the Autoregressive Integrated Moving Average (ARIMA) model to predict future trends in Chinese housing prices. The Chinese real estate market, characterized by its volatility, especially during the post-COVID-19 period, presents a complex environment for buyers and investors. The paper investigates how the ARIMA model is employed to make informed predictions in this uncertain market. Although it has some limitations, such as a heavy reliance on historical data and insensitivity to unexpected macroeconomic shifts, the ARIMA model offers a structure for understanding and anticipating housing price trends. The paper integrates various data sources, including long-term housing price statistics, to build a comprehensive ARIMA model tailored to the nuances of China’s housing market. This essay demonstrates the ARIMA model's utility in aiding stakeholders to make more confident and informed decisions in an increasingly complex and unpredictable market. The analysis further suggests refinements to the ARIMA model, considering the multifaceted nature of the housing market, influenced by macroeconomic factors and public perception. The final goal is to enhance the model's accuracy and reliability, making it an indispensable tool in economic and policy decision-making in China's evolving real estate landscape.

BackgroundThe Department of Critical Care Medicine has the highest risk of nosocomial infection. This study used an autoregressive integrated moving average (ARIMA) model to simulate the prevalence of nosocomial infections in the Department of Critical Care Medicine of Guizhou Province. We also provided a policy basis for the prevention and control of hospital infection in the Department of Critical Care Medicine of Guizhou Province.MethodsThe data of ventilator-associated pneumonia, vascular catheter-related bloodstream infections, and urinary tract intubation-related urinary tract infections in nine tertiary A comprehensive treatment hospitals in Guizhou province from January 2014 to December 2019 were collected. The ARIMA time series model was used to evaluate the model fitting and prediction effects.ResultsAfter comparison, in the Department of Critical Care Medicine of Guizhou Province, the unsurpassed model of ventilator-associated pneumonia was the ARIMA (0,1,1) model, with a residual Ljuing-Box Q test result of Q=10.832 (P=0.865), suggesting it is a white noise sequence and its simulation and prediction effects are beneficial. The best model of vascular catheter-related bloodstream infection was the ARIMA (0,0,1) model, with a residual Ljuing-Box Q test result of Q=14.914 (P=0.602). These results suggest that it is a white noise sequence, and its simulation and prediction effects are sufficient. The optimal model of urinary tract intubation-related urinary tract infection is ARIMA (1,0,0), and the residual Ljuing-Box Q test result is Q=15.042 (P=0.592), suggesting it is a white noise sequence with an accurate simulation and prediction effect.ConclusionsThe ARIMA model can accurately simulate and predict nosocomial infection incidence rate in the Department of Critical Care Medicine of Guizhou Province, and can provide a reference for the prevention and control of nosocomial infections.

The world at large has been confronted with several disease outbreak which has posed and still posing a serious menace to public health globally. Recently, COVID-19 a new kind of coronavirus emerge from Wuhan city in China and was declared a pandemic by the World Health Organization. There has been a reported case of about 8622985 with global death of 457,355 as of 15.05 GMT, June 19, 2020. South-Africa, Egypt, Nigeria and Ghana are the most affected African countries with this outbreak. Thus, there is a need to monitor and predict COVID-19 prevalence in this region for effective control and management. Different statistical tools and time series model such as the linear regression model and autoregressive integrated moving average (ARIMA) models have been applied for disease prevalence/incidence prediction in different diseases outbreak. However, in this study, we adopted the ARIMA model to forecast the trend of COVID-19 prevalence in the aforementioned African countries. The datasets examined in this analysis spanned from February 21, 2020, to June 16, 2020, and was extracted from the World Health Organization website. ARIMA models with minimum Akaike information criterion correction (AICc) and statistically significant parameters were selected as the best models. Accordingly, the ARIMA (0,2,3), ARIMA (0,1,1), ARIMA (3,1,0) and ARIMA (0,1,2) models were chosen as the best models for SA, Nigeria, and Ghana and Egypt, respectively. Forecasting was made based on the best models. It is noteworthy to claim that the ARIMA models are appropriate for predicting the prevalence of COVID-19. We noticed a form of exponential growth in the trend of this virus in Africa in the days to come. Thus, the government and health authorities should pay attention to the pattern of COVID-19 in Africa. Necessary plans and precautions should be put in place to curb this pandemic in Africa.

Stock market volatility is important for investment, option pricing and financial market regulation. In recent years, stock market analysis and prediction have the greatest significance for many professionals in the fields of finance and stock exchange. There are many methods available in the literature to solve the problem of future prediction. The present study provides a detailed comparison of Single Exponential Smoothing (SES) model and Autoregressive Integrated Moving Average (ARIMA) model. Future values are forecasting using SES and ARIMA model. Forecasted values for different iś values are calculated from SES method and ARIMA (0, 2, 3). Also, Mean Square Error (MSE), Mean Absolute Deviation (MAD), Root Mean Square (RMSE) and Mean Absolute Percentage Error (MAPE) are calculated individually for both methods.

Under the background of mass entrepreneurship and innovation, it is of great practical and theoretical significance to predict the Shenzhen Growth Enterprise Market (GEM) price index. In view of the nonlinearity and the non-stationarity of GEM price index a prediction method based on empirical mode decomposition (EMD) and Autoregressive Integrated Moving Average (ARIMA) model is studied. Firstly, the EMD is used to stabilize GEM price index data to make GEM price index data more regular and improve the non-linear and non-stationary characteristics of GEM price index data. Then, the ARIMA model is used to model and predict the decomposed data. The model accuracy evaluation index results of the EMD-ARIMA model in this paper are lower than that of the ARIMA model and the average error rate of the prediction results is also lower than the ARIMA model. The results show that the method proposed in this paper is more accurate than that of direct prediction by only using ARIMA model, which indicates that the EMD-ARIMA method proposed in this paper has higher generalization ability and prediction accuracy.

Nowadays, Saudi government has established several strategic tactics such as Saudi Vision 2030 to predict the future of the country. In order to accomplish a superior growth in the economy of the country, mathematical model and forecasting techniques are important tools. In this study, total annual exports and imports of the Kingdom of Saudi Arabia are forecasted using Artificial Neural Network (ANN) and Autoregressive Integrated Moving Average (ARIMA) models. This paper tries to predict a time series data using ANN and ARIMA models on total annual exports and imports of Kingdom of Saudi Arabia from the year 1968 to the year 2017 with the help of statistical software XLSTAT. The applied models are used to predict some future values of total annual exports and imports of the Kingdom of Saudi Arabia. It is found that the ANN and ARIMA (1, 1, 2) and ARIMA (0, 1, 1) models are suitable for predicting the total annual exports and imports of the Kingdom of Saudi Arabia.

Abstract: This research is driven by the challenges faced by Universitas Lancang Kuning (UNILAK) in attracting applicants amidst intense competition, especially after the government's policy opened independent pathways to State Universities (PTN) from 2022-2023, which impacted private university applicant numbers. To address this and support strategic planning, this study aims to predict the trend of prospective students applying to all study programs at UNILAK for the period 2025-2027. Two time series models were employed: ARIMA (AutoRegressive Integrated Moving Average) and LSTM (Long Short-Term Memory). Applicant data from 2019 to 2024 was used to build the model. The Augmented Dickey-Fuller (ADF) test confirmed the data's stationarity with a p-value of 0.0. ACF and PACF analyses determined the ARIMA parameters as p=1, d=1, q=1. The LSTM model was trained to capture more complex data patterns. ARIMA predictions for 2025, 2026, and 2027 are 3298.66, 3362.33, and 3371.30, respectively. LSTM predictions for the same years are 3335.64, 3476.52, and 3518.42. Evaluation using Root Mean Squared Error (RMSE) showed ARIMA (RMSE=588.72) to be more accurate than LSTM (RMSE=653.96). Nevertheless, LSTM provided a more optimistic prediction. This study concludes that ARIMA is better suited for short-term planning, while LSTM can be used for more ambitious long-term strategies. Keywords: arima; LSTM; applicants; prediction; university Abstrak: Penelitian ini didorong oleh tantangan Universitas Lancang Kuning (UNILAK) dalam menarik pendaftar di tengah persaingan ketat, khususnya setelah kebijakan pemerintah membuka jalur mandiri ke Perguruan Tinggi Negeri (PTN) sejak 2022-2023, yang menyebabkan penurunan jumlah pendaftar di universitas swasta. Untuk mendukung perencanaan strategis, studi ini bertujuan memprediksi tren jumlah calon mahasiswa yang mendaftar ke seluruh program studi di UNILAK untuk periode 2025-2027.Dua model deret waktu digunakan: ARIMA (AutoRegressive Integrated Moving Average) dan LSTM (Long Short-Term Memory). Data jumlah pendaftar dari 2019 hingga 2024 digunakan untuk membangun model. Uji Augmented Dickey-Fuller (ADF) menunjukkan data stasioner dengan p-value 0,0. Analisis ACF dan PACF menentukan parameter ARIMA sebagai p=1, d=1, q=1. Model LSTM dilatih untuk menangkap pola data yang lebih kompleks.Prediksi ARIMA untuk 2025, 2026, dan 2027 adalah 3298.66, 3362.33, dan 3371.30. Prediksi LSTM untuk tahun yang sama adalah 3335.64, 3476.52, dan 3518.42. Evaluasi menggunakan Root Mean Squared Error (RMSE) menunjukkan ARIMA (RMSE=588.72) lebih akurat daripada LSTM (RMSE=653.96). Meskipun demikian, LSTM memberikan prediksi yang lebih optimis. Studi ini menyimpulkan ARIMA lebih cocok untuk perencanaan jangka pendek, sementara LSTM dapat digunakan untuk strategi jangka panjang yang ambisius. Kata kunci: arima; LSTM; pendaftar; prediksi; universitas

The purpose of this study is to demonstrate how to make air quality forecasting to predict the Nitrogen Dioxide quality index in the future. In this paper, we demonstrate exploratory data analysis and compare the performance of the Autoregressive Integrated Moving Average and Exponential Smoothing Model. We used R Language and R Studio to integrate all the datasets, exploratory data analysis, data preparation, performing Autoregressive Integrated Moving Average and Exponential Smoothing methods, model evaluation, and visualization. This study used data from the automatic remote air quality-monitoring station located in an urban area in Madrid, Spain. The dataset in the period from 1 January 2001 to 31 December 2017. The dataset recorded six pollutants such as Nitrogen Dioxide, Particulate Matter 10 micrometres, Sulphur Dioxide, Carbon Monoxide, Ozone and Particulate Matter 2.5 micrometres. In this study, we focus only on Nitrogen Dioxide pollutants. From our model, we saw that exponential smoothing has better accuracy compared to the Autoregressive Integrated Moving Average. We also exposed that Nitrogen Dioxide pollutant shows unhealthy for sensitive group’s level in November to March and has the lowest level in June and July.

Machine learning algorithms such as Support Vector Machine (SVM) and Artificial Neural Network (ANN) are used for machine trading. The problem with SVM and ANN is that it is difficult to determine appropriated features for both models; moreover, it is also time consuming to perform backpropagation for ANN when a number of both features and data increase. However, machine trading is not confined only these two algorithms, but also time series models. In this study, we will employ time series models namely Autoregressive Integrated Moving Average (ARIMA) and Holt – Winters’ Exponential Smoothing (HW) as the guidance for trading since time series models only require time series as an input. We will perform time series analysis to snatch the trading opportunity in the Stock Exchange of Thailand (SET). There are fifty companies on the list of SET50 index; we will choose five amongst them to invest measured by Sharpe Ratio; the top five from this measurement will be selected as invested assets in simulated portfolio. Furthermore, the well-known portfolio optimization framework by Harry Markowitz will be used to ensure that the combination of the invested assets is located on the efficient frontier; the result from this study is favorable as the return generated by these activities outperforms market return; furthermore, manipulating time series into different time lags yields higher return and as well as combining both ARIMA and HW models to help predict stock prices also improves power of prediction of time series models. This study will help retail investor who has limited resource overthrow bias and intuition throughout investment decision process ranging from finding the stocks for investment to capturing market movement for trading opportunity.

Hydraulic support plays a key role in ground control of longwall mining. The smart prediction methods of support load are important for achieving intelligent mining. In this paper, the hydraulic support load data is decomposed into trend term, cycle term, and residual term, and it is found that the data has clear trend and period features, which can be called time series data. Based on the autoregression theory and weighted moving average method, the time series model is built to analyze the load data and predict its evolution trend, and the prediction accuracy of the sliding window model, ARIMA (Autoregressive Integrated Moving Average) model, and SARIMA (Seasonal Autoregressive Integrated Moving Average) model to the hydraulic support load under different parameters are evaluated, respectively. The results of single-point and multipoint prediction test with various sliding window values indicate that the sliding window method has no advantage in predicting the trend of the support load. The ARIMA model shows a better short-term trend prediction than the sliding window model. To some extent, increasing the length of the autoregressive term can improve the long-term prediction accuracy of the model, but it also increases the sensitivity of the model to support load fluctuation, and it is still difficult to predict the load trend in one support cycle. The SARIMA model has better prediction results than the sliding window model and the ARIMA model, which reveals the load evolution trend accurately during the whole support cycle. However, there are many external factors affecting the support load, such as overburden properties, hydraulic support moving speed, and worker’s operation. The smarter model of SARIMA considering these factors should be developed to be more suitable in predicting the hydraulic support load.

The Covid-19 pandemic has caused severe economic depression and has disrupted the supply chains of various industries. The automobile industry which contributes significantly to the Indian economy was gravely hit due to the lockdowns, semiconductor shortage and the uncertainty associated with the pandemic. This research paper analyses the effect of Covid-19 on the automobile sales in India using the time series modelling approach. The data recorded by SIAM from 2012 to 2019 was used to develop the Autoregressive Integrated Moving Average (ARIMA) model following the Box-Jenkins methodology. ARIMA model (2, 1, 3) was chosen as it had the lowest AIC and BIC criteria. This model was used to forecast the sales from 2020 to 2021 to give a picture of the expected automobile sales had the pandemic not occurred. The forecasted data from the model developed has then been compared with actual automobile sales data during the pandemic to gauge the level of impact Covid-19 had on the Indian automobile industry. The paper also explores the associated challenges that the automobile industry had to face due to the pandemic.

The dust produced by transportation roads is the primary source of PM2.5 pollution in opencast coal mines. However, China’s opencast coal mines lack an efficient and straightforward construction scheme of monitoring and management systems and a short-term prediction model to support dust control. In this study, by establishing a PM2.5 and other real-time environmental information to monitor, manage, visualize and predict the Internet of things monitoring and prediction system to solve these problems. This study solves these problems by establishing an Internet of things monitoring and prediction system, which can monitor PM2.5 and other real-time environmental information for monitoring, management, visualization, and prediction. We use Lua language to write interface protocol code in the APRUS adapter, which can simplify the construction of environmental monitoring system. The Internet of things platform has a custom visualization scheme, which is convenient for managers without programming experience to manage sensors and real-time data. We analyze real-time data using a time series model in Python, and RMSE and MAPE evaluate cross-validation results. The evaluation results show that the average RMSE of the ARIMA (4,1,0) and Double Exponential Smoothing models are 12.68 and 8.34, respectively. Both models have good generalization ability. The average MAPE of the fitting results are 10.5% and 1.7%, respectively, and the relative error is small. Because the ARIMA model has a more flexible prediction range and strong expansibility, and ARIMA model shows good adaptability in cross-validation, the ARIMA model is more suitable as the short-term prediction model of the prediction system. The prediction system can continuously predict PM2.5 dust through the ARIMA model. The monitoring and prediction system is very suitable for managers of opencast coal mines to prevent and control road dust.

This article presents a comprehensive study of sales predictions using time series analysis, focusing on a case study of Walmart sales data. The aim of this study is to evaluate the effectiveness of various time series forecasting techniques in predicting weekly sales data for Walmart stores. Leveraging a dataset from Kaggle comprising weekly sales data from various Walmart stores around the United States, this study explores the effectiveness of time series analysis in forecasting future sales trends. Various time series analysis techniques, including Auto Regressive Integrated Moving Average (ARIMA), Seasonal Auto Regressive Integrated Moving Average (SARIMA), Prophet, Exponential Smoothing, and Gaussian Processes, are applied to model and forecast Walmart sales data. By comparing the performance of these models, the study seeks to identify the most accurate and reliable methods for forecasting retail sales, thereby providing valuable insights for improving sales predictions in the retail sector. The study includes an extensive exploratory data analysis (EDA) phase to preprocess the data, detect outliers, and visualize sales trends over time. Additionally, the article discusses the partitioning of data into training and testing sets for model evaluation. Performance metrics such as Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) are utilized to compare the accuracy of different time series models. The results indicate that Gaussian Processes outperform other models in terms of accuracy, with an RMSE of 34,116.09 and an MAE of 25,495.72, significantly lower than the other models evaluated. For comparison, ARIMA and SARIMA models both yielded an RMSE of 555,502.2 and an MAE of 462,767.3, while the Prophet model showed an RMSE of 567,509.2 and an MAE of 474,990.8. Exponential Smoothing also performed well with an RMSE of 555,081.7 and an MAE of 464,110.5. These findings suggest the potential of Gaussian Processes for accurate sales forecasting. However, the study also highlights the strengths and weaknesses of each forecasting methodology, emphasizing the need for further research to refine existing techniques and explore novel modeling approaches. Overall, this study contributes to the understanding of time series analysis in retail sales forecasting and provides insights for improving future forecasting endeavors.

Statistical analysis of aerosols over the Gangetic–Himalayan region using ARIMA model based on long-term MODIS observations

Time series analysis of aerosol optical depth over New Delhi using Box–Jenkins ARIMA modeling approach