Autoregressive Integrated Moving Average Method Research Articles

Predicting the Number of Forest and Land Fire Hotspot Occurrences Using the ARIMA and SARIMA Methods

Forests are an area and part of the environmental cycle that is very important for survival because forests are areas on Earth that regulate the balance of the ecosystem. Forest fires rank second only to illegal logging in Indonesia's list of forest destruction causes. Forest fires can occur due to two factors, namely natural and human factors. Therefore, the hotspot factor that can cause forest fires is an independent variable. The population of hotspots in the West Kalimantan region in 2020 amounted to 1,416 spots. This study aims to predict the number of hotspot occurrences on land and forests that cause fires before the fires spread and are challenging to overcome or extinguish. The method to indicate the number of hotspot occurrences uses the Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA) methods. Modeling ARIMA (0,1,1) and SARIMA (0,1,1) (2,2,1)12 obtained Root Mean Square Error (RMSE) evaluation results for ARIMA of 6.61 while SARIMA of 7.61. The ARIMA's Mean Squared Error (MSE) evaluation value is 43.70, and the SARIMA is 58.05. Based on these results, it can be concluded that the ARIMA model provides excellent and accurate performance in describing the trend of hotspot events that will occur in the future with a smaller RMSE value compared to SARIMA.

Efektivitas Metode Hibrida ARIMA-MLP untuk Peramalan Nilai Tukar Petani

The agricultural sector remains a crucial pillar of Indonesia’s economy, making the most significant contribution. Still, the situation of farmers, primarily the elderly, indicates physical limitations and low income leading to high poverty levels, coupled with fluctuations in the Farmer Exchange Rate (FER) annually tending to decline in D.I. Yogyakarta, indicating losses due to increased production costs. This research aims to assess the effectiveness of the Hybrid Autoregressive Integrated Moving Average (ARIMA) – Multilayer Perceptron (MLP) method in forecasting NTP in D.I. Yogyakarta. This is based on the analysis of comparing the accuracy values of forecasts using Mean Absolute Percentage Error (MAPE) evaluation or through visualizing the forecast graphs generated between the ARIMA and Hybrid ARIMA-MLP methods. The combination (hybrid) of ARIMA and MLP methods addresses the complexity of time series, where ARIMA anticipates NTP changes by handling linear patterns. At the same time, MLP improves forecast accuracy by managing more complex patterns (both linear and nonlinear). Thus, it can provide more accurate information about the welfare development of farmers. The results show that the Hybrid ARIMA-MLP method is significantly better than the individual ARIMA method, with the obtained model being Hybrid ARIMA-MLP (12-5-10-2) and an accuracy of 99.993%.

COVID-19 İnsidans Oranlarını Modelleme ve Öngörümleme: Fransa'da Gözetim Başlangıcından Beri Akut Solunum Enfeksiyonları (ARI) Zaman Serisi Analizi

Objective: This study aims to address the challenges of planning and managing the trajectory of the COVID-19 pandemic by evaluating the predictive abilities of three distinct forecasting models. The primary focus is on the ATA univariate forecasting method, ARIMA (AutoRegressive Integrated Moving Average), and ETS (Error-Trend-Seasonality) models. These models are applied to a meticulously collected dataset comprising Acute Respiratory Infections (ARI) incidence rates in France, systematically collected since the initiation of surveillance. Methods: The purpose of the study was to conduct a comprehensive evaluation of forecasting models using the selected dataset to achieve its objective. The focus was on comparing the accuracy and performance of ATA univariate forecasting, ARIMA, and ETS models in predicting COVID-19 incidence rates. Additionally, the study incorporated a combination approach proven to be effective in enhancing forecasting performance. Results: According to the results obtained regarding forecast performance, the univariate models indicate that the ATA method exhibits the highest performance, while observations reveal that combinations of ATA and ARIMA methods enhance forecast accuracy. Conclusions: In summary, the most accurate approach for forecasting future Covid-19 incidence rates, specifically those derived from Acute Respiratory Infections (ARI), has been a combination of the high-accuracy methods ATA and ARIMA. These findings enhance our understanding of the trajectory of the pandemic, providing a foundation for strategic planning and effective management.

PREDIKSI KINERJA KEUANGAN PERUSAHAAN ASURANSI SYARIAH MENGGUNAKAN METODE ARIMA, EXPONENTIAL SMOOTHING, DAN HYBRID

Insurance is a contract made by an insurance company with a policy holder. One way to assess the stability of company is its financial performance. The aim of this study is to predict the financial performance of sharia insurance company using two factors, namely ROA (Return on Assets) and ROE (Return on Equity). The secondary data used in this research is derived from the quarterly financial reports of an Islamic insurance company from the year 2013 to 2022. The study used ARIMA (Autoregressive Integrated Moving Average), Exponential Smoothing (ETS), and Hybrid methods. The results showed that the ARIMA model is the best way to better predict both ROA and ROE. Compared to other methods that have been evaluated, the ARIMA model showed more accurate results in predicting financial performance measured through ROAs and ROEs. Predictions show that the ROA value is relatively stable, indicating that the company is efficient in managing resouces while the ROE value tends to fail, which indicates that the identified company is less good at delivering returns to shareholders. Therefore, it can be one of considerations for people who wants to buy insurance or invest

SHORT-TERM FORECASTING DAILY ELECTRICITY LOADS USING SEASONAL ARIMA PATTERNS OF GENERATION UNITS AT PT. PLN (PERSERO) TARAKAN CITY

Electrical power requirements at load centers tend to change over time, so the State Electricity Company (PLN) as a provider of electrical energy must be able to predict electrical load requirements every day. The city of Tarakan as a reference center in the northern region of Indonesia is developing rapidly. Along with this growth, the need for electric power is of course also increasing, so we must be able to provide an economical and reliable electric power supply system. This research aims to predict the electricity load at PT. PLN (Persero) Tarakan City. The author will carry out short-term forecasting using time series data in the form of daily electrical power usage data using the Autoregressive Integrated Moving Average (ARIMA) method. The ARIMA method or often called the Box-Jenkins technique shows that this method is suitable for predicting a number of variables quickly, simply and cheaply because it only requires variable data to be predicted. Analysis based on the Box-Jenkins time series taking into account the influence of seasonal patterns. The prediction results show that the data contains seasonal elements with the best model being SARIMA with a MAPE of 3 percent.

Comparison of Three Prediction Models for Predicting Chronic Obstructive Pulmonary Disease in China.

To predict the future number of patients with chronic obstructive pulmonary disease (COPD) in China and compare the three prediction models. A generalized additive model (GAM), autoregressive integrated moving average (ARIMA) model, and curve-fitting method were used to fit and predict the number of patients with COPD in China. Data on the number of patients with COPD in China from 1990 to 2019 were obtained from the Global Burden of Disease (GBD) database. The coefficient of determination (R2), mean absolute error (MAE), mean absolute percentage error (MAPE), root mean squared error (RMSE), relative error of prediction, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC) were used to evaluate and compare the fitting effect, prediction effect, and reliability of the three models. The GAM, ARIMA, and curve-fitting methods could predict future trends in COPD in China. The performance of the GAM is the best among the three models, whereas the curve fitting method is the worst, and the ARIMA (0,1,2) model is in between. The prediction results of the three models showed that the number of patients with COPD in China is expected to increase from 2020 to 2025. GAM and AIRMA models are recommended for predicting the future prevalence of COPD in China. The number of patients with COPD in China is expected to increase in the next few years. The prevention and control of COPD in China still needs to be strengthened. Using appropriate models to predict future trends in COPD will provide support for health policymakers.

Forecasting the Export Value of Oil and Gas in Indonesia using Autoregressive Integrated Moving Average (ARIMA)

This study aims to utilize the ARIMA method to predict the value of Indonesia's oil and gas exports. As quantitative research, it employs secondary data sourced from the Central Bureau of Statistics of the Republic of Indonesia's website. The data spans January 2010 to March 2022 and are presented on a monthly basis. Through the results and discussion, three ARIMA models were established, namely ARIMA (1,1,0), ARIMA (0,1,1), and ARIMA (1,1,1). Among these models, the ARIMA (0,1,1) model with an AIC value of 2047.65 was found to be the most suitable for forecasting Indonesia's oil and gas exports. The forecasted values for the next five periods were 1254.124 (April 2022), 1309.678 (May 2022), 1289.236 (June 2022), 1296.758 (July 2022), and 1293.990 (August 2022).

Unfolding the potential of the ARIMA model in forecasting maize production in Tanzania

PurposeThis paper was set to develop a model for forecasting maize production in Tanzania using the autoregressive integrated moving average (ARIMA) approach. The aim is to forecast future production of maize for the next 10 years to help identify the population at risk of food insecurity and quantify the anticipated maize shortage.Design/methodology/approachAnnual historical data on maize production (hg/ha) from 1961 to 2021 obtained from the FAOSTAT database were used. The ARIMA method is a robust framework for forecasting time-series data with non-seasonal components. The model was selected based on the Akaike Information Criteria corrected (AICc) minimum values and maximum log-likelihood. Model adequacy was checked using plots of residuals and the Ljung-Box test.FindingsThe results suggest that ARIMA (1,1,1) is the most suitable model to forecast maize production in Tanzania. The selected model proved efficient in forecasting maize production in the coming years and is recommended for application.Originality/valueThe study used partially processed secondary data to fit for Time series analysis using ARIMA (1,1,1) and hence reliable and conclusive results.

Using the Arima Method with Minitab Applications for Forecasting Work Order Projects of Casting Construction (Case Study: PT. Bumi Sarana Beton)

The ARIMA method is a non-stationary homogeneous time series model that uses the procedure for applying the Autoregressive model or scheme and the Moving Average in preparing its forecasts. The purpose of this study was to determine the application of the Autoregressive Integrated Moving Average (ARIMA) method and minitab application at PT. Bumi Sarana Beton in planning the estimated number of work orders. It can be concluded that for K-225 the best model is the ARIMA model (2,0,0) because it has the lowest MSE value and for K-400 the best model to use is the ARIMA model (2,0,2 ) because it has the smallest value. From the results of the research that has been done, it can be concluded that the production of K-225 in 2022 is 3573.50 m3, while using the ARIMA method in 2023 the total production is 3920.61 m3 and in 2024 the total production is 3573.50 m3. to 3824.36 m3. Production of K-400 in 2022 was 2015.00 m3, while using the ARIMA method in 2023 a total production of 1857.07 m3 was obtained and in 2024 a total production of 2045.89 m3 was obtained.

Forecasting the Fuel Consumption and Price for a Future Pandemic Outbreak: A Case Study in the USA under COVID-19

The COVID-19 epidemic and the measures adopted to contain it have had a significant impact on energy patterns throughout the world. The pandemic and movement restrictions led to unpredictable fluctuations in power systems demand and the fuel price for a delayed period. Monkeypox, another viral disease, appeared during the post-COVID period. It is assumed that the outbreak of monkeypox is unlikely due to the implication of preventive measures experienced from COVID-19. At the same time, the probability of an epidemic cannot be blindly overlooked. This paper aims to examine and analyze historical data to look at how much petroleum fuel was used for generating power and how the price of petroleum fuel changed over seven years, from January 2016 to August 2022. This period covers the time before the COVID-19 pandemic, during the pandemic, and after the pandemic. Several time-series forecasting models, including all four benchmark methods (Mean, Naive, Drift, and Snaive), Seasonal and Trend decomposition using Loess (STL), Exponential Smoothing (ETS), and Autoregressive Integrated Moving Average (ARIMA) methods have been applied for both fuel consumption and price prediction. The best forecasting method for fuel price and consumption has been identified among these methods. The best forecasting method for fuel consumption observed is ETS based on the RMSE value, which is 799.59, and the ARIMA method for fuel price, with RMSE 4.67. The paper also utilizes the ARIMAX model by incorporating multiple exogenous variables, such as monthly mean temperature, mean fuel price, and mileage of vehicles traveling during a certain period of pandemic lock-down. It will assist in capturing the non-smooth and stochastic pattern of fuel consumption and price due to the pandemic by separating the seasonal influence and, thus, provide a prediction of the consumption pattern in the event of any future pandemic. The novelty of the article will assist in exploring the potential energy demand in terms of cost and consumption of fuel during any pandemic period, considering the associated abnormalities.

PERAMALAN JUMLAH PRODUKSI BATUBARAMENGGUNAKAN METODE ARIMA (STUDI KASUS: PT ARUTMIN INDONESIA SITE ASAM-ASAM)

In the world of industry and investment, the mining sector has an important role in producing natural resources in Indonesia. South Kalimantan Province is one of the regions which is very famous for being rich in natural resources, especially in the mining sector, especially energy minerals in the form of coal. In recent years, coal production at PT. Arutmin Asam Asam experienced an unstable amount of production. This study aims to obtain the best model and predict the amount of coal production at PT Arutmin Indonesia Site Asam-Asam, South Kalimantan Province using the ARIMA (Autoregressive Integrated Moving Average) method. The best Autoregressive Integrated Moving Average (ARIMA) method for predicting coal production at PT Arutmin Indonesia Site Asam-Asam is the ARIMA model (2,1,3) with a smaller Root Mean Square Error (RMSE) compared to other models, namely 92029.74. Forecasting results of coal production at PT Arutmin Arutmin Site Asam-Asam from January to August 2023 tend not to indicate stability or there will be increases and decreases in the forecasting results obtained.

Forecasting Bulk Cooking Oil Prices for 20 District/Cities in North Sumatra Using Autoregressive Integrated Moving Average (ARIMA) Method

Abstract— Frying is a popular method of food preparation in Indonesian society, whether it's for home-cooked meals or snacks bought outside. In Indonesia, there is bulk cooking oil available without a brand, which is cheaper. As a result, the usage of bulk cooking oil is higher compared to branded cooking oil. According to data published by BPS (Central Statistics Agency) in 2021, the per capita consumption of cooking oil in Indonesia reached 0.393L/Capita/week. The consumption of cooking oil in North Sumatra is also relatively high, but the poverty rate in North Sumatra is still high, with a figure of 1,268.19 thousand people.
 ARIMA method has advantages where the forecasting follows the data pattern and is flexible with relatively high accuracy. It is a quick and simple process (Hutasuhut, 2014). In ARIMA, there are two ways to determine the model, which are using the Auto Arima function and analyzing the ACF & PACF plots. The data used is the bulk cooking oil price data from January 2021 to January 2023 in Excel format, obtained from the Main Commodity Price System of North Sumatra.
 Based on the conducted research, the accuracy of the models generated by the Auto Arima function and the ACF & PACF plots are relatively similar. However, in some cases, the models generated by the Auto Arima function are not significant, although they have higher accuracy than the models generated from the ACF & PACF plots. The highest accuracy level is in Nias Utara district with an accuracy of 99.67% using the ARIMA(3,1,5) model, while the lowest accuracy level is in Nias Barat district with an accuracy of 75.26% using the ARIMA(2,0,0) model.
 Index Terms— ARIMA, Bulk Cooking Oil, Auto Arima, ACF&PACF Plot.

Modeling and Forecasting Inflation in Nigeria: A Time Series Regression with ARIMA Method

This study uses time series regression with autoregressive integrated moving average (ARIMA) modeling to establish a model for forecasting inflation in Nigeria for the period 1981-2020. Akaike Information Criterion Corrected (AICC) and Bayesian Information Criterion (BIC) were used to select the best model among competing models. Through these methods, regression with ARIMA (0,0,1) error was selected as the most parsimonious model for inflation forecasting in Nigeria. The results of the out-sample-forecast show that a high inflation rate will be experienced by the end of 2023, and between 2024 and 2030, the inflation rate will be alternating but will maintain a lower rate than that of 2023.

PREDICTING MARKET SEGMENTS FROM TWITTER DATA USING ARIMA TIME SERIES ANALYSIS

Twitter data on social media can be used to predict potential market segments in the future. The continuous nature of Twitter data and data collection sequentially over a certain period uses a text mining process with time series data that matches the actual data. The problem to be solved is to forecast market segments using Twitter data with greater accuracy. Various market segments that are of business interest through the tweets of individual Twitter users have not been utilized optimally. Based on the Twitter data pattern, the research shows that the data pattern is not stationary, so to analyze the data, it is necessary to use the Autoregressive Integrated Moving Average (ARIMA) method. This study aims to analyze time series data from Twitter data and predict market segment predictions using the ARIMA method. The ARIMA method is a method that has advantages in flexibility, the ability to handle stationary and non-stationary data, as well as short-term forecasting with a statistical approach in various time series data forecasting applications. Prediction results using the ARIMA method with an accuracy rate of 94.88%. There are several ways to measure model validation including MAD, MSE, RMSE, F1-Score, and MAPE. In this study, MAD, MSE, and MAPE were used with an accuracy rate of 5.22%. This study succeeded in applying the ARIMA method to time series data from Twitter to forecast market segments with high accuracy, opening opportunities for utilizing Twitter data in business strategy.

Predictive quality assurance for linear accelerator target failure using statistical process control

The performance of a linear accelerator (Linac) depends on the integrity of its x-ray target. The sudden failure of its target not only breaks down the Linac but also could contribute significant disruptions to patient care. This work is to develop a predicative quality assurance (QA) method using Statistical Process Control (SPC) and AutoRegressive Integrated Moving Average (ARIMA) modeling to identify the risk of target failure before it occurs. In the past years, we observed two incidents of target failure among our Linacs. Retrospectively, we collected past daily QA data (from both open fields and enhanced dynamic wedge (EDW) measurements) and analyzed its historical trend using methods of SPC and ARIMA. SPC is a technique that monitors process performance based on statistical analysis. ARIMA is a time-series forecasting algorithm that can be used to estimate future values based on its past pattern. Both have been evaluated for predictive QA in radiotherapy. Application of SPC on open beam QA data would not yield an early warning signal to the pending target failures. However, when the same SPC methodology applies to EDW measurements, the control limits were breached a couple of weeks before the target failed. EDW mechanism introduces nonuniform magnification factors over its wedge-directed beam profiles and is responsible for the sensitivity of its profile to changing beam properties induced by a degrading target. Further extension of the warning period may be possible by using ARIMA modeling. Predicative QA for EDW daily data using SPC and ARIMA methods may provide an early QA warning to incoming Linac target failure.

Forecasting of daily new lumpy skin disease cases in Thailand at different stages of the epidemic using fuzzy logic time series, NNAR, and ARIMA methods.

Lumpy skin disease (LSD) is an important transboundary disease affecting cattle in numerous countries in various continents. In Thailand, LSD is regarded as a serious threat to the cattle industry. Disease forecasting can assist authorities in formulating prevention and control policies. Therefore, the objective of this study was to compare the performance of time series models in forecasting a potential LSD epidemic in Thailand using nationwide data. For the forecasting of daily new cases, fuzzy time series (FTS), neural network auto-regressive (NNAR), and auto-regressive integrated moving average (ARIMA) models were applied to various datasets representing the different stages of the epidemic. Non-overlapping sliding and expanding window approaches were also employed to train the forecasting models. The results showed that the FTS outperformed other models in five of the seven validation datasets based on various error metrics. The predictive performance of the NNAR and ARIMA models was comparable, with NNAR outperforming ARIMA in some datasets and vice versa. Furthermore, the performance of models built from sliding and expanding window techniques was different. This is the first study to compare the forecasting abilities of the FTS, NNAR, and ARIMA models across multiple phases of the LSD epidemic. Livestock authorities and decision-makers may incorporate the forecasting techniques demonstrated herein into the LSD surveillance system to enhance its functionality and utility.

Trend Forecasting of the Top 3 Indonesian Bank Stocks Using the ARIMA Method

The number of investors in Indonesia increases annually. This is due to the growing popularity of investing, particularly stock investment. There are currently three largest equities in the banking industry, namely BBCA, BBRI, and BMRI. Stock prices fluctuate and form multiple patterns of price movements; therefore, investors must be able to recognize the patterns and trends of securities on the capital market in order to plan long-term investments, maximize potential profits, and reduce the risk of investment losses. In addition to knowing the trajectory of the stock market's trend, investors rely heavily on forecasting. Forecasting is necessary so that investors can anticipate future prices. The Autoregressive Integrated Moving Average (ARIMA) method is a frequently used method for forecasting time series data. In general, ARIMA is represented by the formula ARIMA (p, d, q), where p represents the Autoregressive (AR) order, d represents the difference, and q represents the Moving Average (MA) order. The trend of BBCA, BBRI, and BMRI stock data was effectively predicted using the ARIMA method. The results of this study are presented as diagrams of actual and forecasted data for the next 12 periods, as well as predictions of the optimal purchase price points for stocks. The ARIMA model of each stock also generates a low MAPE error value, with MAPE values of 4% for BBCA, 5% for BBRI, and 7% for BMRI. The MAPE value derived by each model is incorporated into the MAPE value with a high degree of precision, as it falls below 10%.

Indonesian Consumer Price Index Forecasting Using Autoregressive Integrated Moving Average

The Consumer Price Index is one of the indicators used to confirm financial success in inflation management. This study aims to help determine the CPI prediction value in Indonesia for the next twelve periods in a month using the ARIMA (Autoregressive Integrated Moving Average) method using the data from January 2015 to March 2022. The results obtained show that the best model that can be used for forecasting is the ARIMA model (2,1,2) with drift with Akaike's Information Criterion (AIC) values of 2190.84. The results of Indonesia's accurate CPI forecasting can be used to assess inflation management for policymaking in the context of controlling inflation.It can be concluded that Based on the analysis, the optimal ARIMA model for forecasting Indonesia's CPI is ARIMA (2,1,2) with drift, aiding in evaluating inflation management for policymaking

Forecasting Pendapatan UMKM: Strategi Tiga UMKM Unggulan di Kalurahan Hargorejo Dalam Kontribusi Terhadap Kalurahan Hargorejo, Kapanewon Kokap Kulon Progo Menggunakan Metode Arima

This research aims to forecast the income of MSMEs (Micro, Small and Medium Enterprises), namely Batik Ngudi Rejeki, Geblek Mbak Yuni, and Better Snack in Hargorejo Village, Kapanewon Kokap, Kulon Progo using the ARIMA (AutoRegressive Integrated Moving Average) method. MSME income data from 2019 to 2022 is used in this analysis. The ARIMA method is used to identify patterns and trends in revenue data, as well as predict future earnings. The results of the ARIMA analysis show forecasting the income of MSMEs Batik Ngudi Rejeki, Geblek Mbak Yuni, and Better Snack in the contribution of the Hargorejo, Kokap, Kulon Progo Villages for the 2023 period. The results of this forecasting can provide insight into the development of MSME income in the future and become a reference for business owners in more effective business planning, decision making, and strategy development. The data used is historical data on MSME income in a time series, where the process is analyzed using statistical software Eviews 12 to produce an accurate forecasting model.

APLIKASI MODEL ARIMA GARCH DALAM PERAMALAN DATA NILAI TUKAR RUPIAH TERHADAP DOLAR TAHUN 2017-2022

The Indonesian rupiah (IDR) exchange rate is used to gauge Indonesia's economic stability. Maintaining the IDR exchange rate's stability is critical since it has a direct impact on Indonesia's national monetary situation, particularly during the Covid-19 pandemic. Forecasting the rupiah exchange rate is important to do and is one way to assess government policy. The data series to be used here are IDR exchange rate from the Yahoo Finance. It consists of 271 data taken from August 2017 to October 2022. This study aims to use the Autoregressive Integrated Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) modeling method using the R-studio software and predict the IDR exchange rate. The ARIMA method describes the data based on a certain time series. ARCH-Lagrange Multiplier (ARCH-LM) was applied on the residuals of the best ARIMA model to test whetoer the data is heteroscedasticity. The testing result shows that the residual of the IDR exchange rate is heteroscedasticity. Therefore, the GARCH model can be used to handle it. The results of this study are obtained for the ARIMA(2,1,3) GARCH(3,6) model as the best and describe the actual data pattern with a mean absolute percentage error (MAPE) forecasting value is 1,99%.