Sugar Production Forecasting System in PTPN XI Semboro Jember Using Autoregressive Integrated Moving Average (ARIMA) Method

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.

This study aims to analyze and predict the number of Elementary School Students using Autoregressive Integrated Moving Average (ARIMA) method using data from the last 17 years, case studies in three provinces namely Bali, West Nusa Tenggara (NTB), and East Nusa Tenggara (NTT). This type of research is quantitative by comparing the final value on the first graph to the fourth graph to analyze on the graph what the predictive value is most accurate. Based on the results of the simulation of the number of elementary school students in Bali, NTB, and NTT provinces using the G-MFS application program and mathematical model calculations that the predicted results in 2021 on the data of the number of elementary school students in Bali province amounted to 417,805.40 with a percentage decrease of 0.1%, then the predicted result in the data of the number of elementary school students in NTB province of 512,381.76 with a percentage increase of 1.0%. The predicted result on the data of the number of elementary school students in NTT province amounted to 705,335.11 with an increase of 1.0%. The results of the forecasting of the number of elementary school students are expected to provide important information for the government to improve development in the education sector, especially at the elementary school education level in one way that is to improve the quality of educational infrastructure and many more developments that need to be done by the number of students in the future.

ARIMA forecasting of primary energy demand by fuel in Turkey

Indonesia as an archipelago faces complex logistical challenges, especially in the distribution of fuel oil (BBM) to remote areas. This research aims to forecast fuel logistics needs in the Anambas Islands Regency using the Autoregressive Integrated Moving Average (ARIMA) method. Forecasting was carried out on three main aspects: fuel demand (Type 1 and Type 2) per sub-district, sea wave height, and number of vehicles by type. The results show that the three elements have a relatively stable pattern during the forecasting period until June 2025, with the dominant ARIMA model configurations (0,1,0) and (0,1,1). In these configurations, p = 0 indicates no autoregressive component, d = 1 represents first differencing to remove trends, and q = 0 or 1 reflects either the absence or presence of a simple moving-average term to smooth short-term fluctuations. These parameter combinations suggest that the time series data exhibit stable linear trends with limited short-term volatility. Fuel demand per sub-district shows a steady trend, sea waves are in the low to medium category, and the number of vehicles does not experience significant spikes. This stability supports efficient and predictive data-based fuel distribution planning. The research also recommends the integration of forecasting results into the development of an adaptive and sustainable decision-making system in the islands.

Tuberculosis (TB) remains a significant public health concern in many parts of the world, including Indonesia.Accurate forecasting of the number of TB patients is crucial for health clinics to prepare resources and optimize patient care.This study aims to forecast the number of tuberculosis patients visiting the Mitra Sehat Clinic using the AutoRegressive Integrated Moving Average (ARIMA) method.The ARIMA model is widely used for time series forecasting due to its ability to capture the dynamics of various types of data.Data on patient visits to the clinic over a specified period were collected and analyzed to develop an appropriate ARIMA model.The steps for using the ARIMA method are using data from the required sample from patients visiting with Tuberculosis diagnosed in January 2021 -August 2024, determining the type of time series data pattern, then conducting a stationarity test, determining the ARIMA model, calculating and analyzing the accuracy of the model used, then forecasting the number of outpatient visits.Based on the calculation, the best ARIMA model for this forecasting is (1, 0, 1) with an error value of 42,0276.The results indicate that the ARIMA model can provide reliable forecasts, assisting healthcare providers in managing resources and improving service delivery for TB patients.This research contributes to the strategic planning of health services and highlights the importance of predictive analytics in healthcare.

Time series modeling and analysis provides means of predicting the future and has been widely used in a variety of fields ranging from seismology for predicting earthquake and volcanic eruption, to finance for risk assessment, and to quantum information processing. The conventional integer order models can only capture short-range dependence; for example, Poisson processes, Markov processes, autoregressive (AR), moving average (MA), autoregressive moving average (ARMA) and autoregressive integrated moving average (ARIMA) processes. In time series analysis, one of the conventional assumptions is that the coupling between values at different time instants decreases rapidly as the time difference or distance increases. However, there are situations where strong coupling between values at different times exhibit properties of long range dependence which cannot be processed by the conventional time series analysis. Typical examples of long range dependence signals include financial time series, underwater noise, electroencephalography (EEG) signal, etc. ARFIMA, a fractional order signal processing technique, is the generalization of the conventional integer order techniques, namely, ARIMA and ARMA methods. Hence, it is capable of capturing both short-range dependence and long-range dependence in signals. Compared to conventional integer order models, the ARFIMA model gives a better fit and result when dealing with the data which possess the long range dependence property. In this paper, we investigate the application of the ARFIMA as well as AR methods to model EEG signals obtained from different brain channels. We analyze the resulting correlations for comparison the benefits of ARFIMA over AR on the EEG data exhibiting the long range dependency property. The results showed that the prediction results have a better performance compared to the conventional ARMA models.

Abstract-The paper describes the statistical methodology of multiple linear regression (MLR) and autoregressive integrated moving average (ARIMA) methods for mid term load forecasting of the country. The mid term load forecast has many applications such as maintenance scheduling, fuel reserve planning and unit commitment. However, the monthly peak load is a nonlinear, and non-stationary signal. Therefore, this paper proposed a statistical methodology to solve this problem which using multiple linear regression, and autoregressive integrated moving average, based on historical series of electric peak load, weather, and new economic variables such as consumer price index, and industrial index. This paper focuses on the forecasting of monthly peak load for 12 months ahead. This study focused on the mid term load forecasting of peak load demand for Thailand. Finally, we compared between MLR and ARIMA method that the results obtained the autoregressive integrated moving average method proves to be the best accuracy more than the multiple linear regression method.

The Autoregressive Integrated Moving Average (ARIMA) method was used in this study to forecast the number of Old Age Savings (THT) insurance claims at PT ASABRI (Persero) Makassar Branch. The data used consisted of 37 monthly observations of THT claims from May 2022 to May 2025. The model identification results indicate that the ARIMA (1,1,0) model is appropriate, with a p-value <0.05 and residuals similar to white noise. This forecast was made for June to December 2025. According to the Mean Absolute Percentage Error (MAPE) value of 17,7123%, this model has a fairly high level of accuracy. It is hoped that the results of this study will assist businesses in making financial decisions and strategic planning.

Over the years, the United States Dollar, European Euro, and the British Pound Sterling exchange rate to Nigerian Naira has been on the increase. It has become pertinent to identify robust models that will help to cope with the variability associated with the increase in exchange rate. Several studies showed the ARIMA method to be highly useful in modelling and forecasting exchange rates. However, not much work has been done on modelling and forecasting Nigerian Exchange rate using machine learning models which is the focus for this study. The models we used in the study are; the Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN), and ARIMA-ANN models. Secondary data obtained from Central Bank of Nigeria (CBN) were used. The results showed that the most appropriate model out of the three time series models considered for these exchange rates is the ARIMA-ANN which produced a better forecast compared to ARIMA and ANN. This conclusion was based on the lowest standards of prediction accuracy which ARIMA-ANN produced the lowest Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) values for the three different currencies we compared against the naira. Based on the continuous increase in the Nigerian Exchange Rate, the Government and policymakers should take economic measures to avoid the persistent downfall of the Nigerian Naira.

Nowadays , nonlinear time series and artificial neural networks (ANN) models are used for forecasting in the field of business, agriculture and soon. Recent studies have shown, ANN have been successfully used for forecasting of financial and agriculture data series The classical methods used for time series prediction like Box-Jenkins or ARIMA assumes that there is a linear relationship between inputs and outputs. ANN have more advantages that can approximate to model both linear and nonlinear structures in time series, they are not able to handling both structures equally well. The autoregressive integrated moving average (ARIMA) model and two ANN models namely, Radial basis function neural networks (RBFNN), and Elman recurrent neural networks (ERNN) methods were applied to Hyderabad airport traffic data. The data obtained for 15 years from 2002–2003 to 2016–2017 about domestic and international passenger of International Airport of Hyderabad, India. In this research paper, we compared the performances of ARIMA, RBFNN and ERNN were based on three measures: mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The results showed that RBFNN obtained the smallest MAE, MAPE and RMSE in both the modeling and forecasting processes. The performances of the three models ranked in ascending order were: ARIMA, ERNN and the RBFNN model. Keywords: T ime series, forecasting, artificial neural networks, ARIMA models, radial basis function neural networks, and Elman recurrent neural networks Cite this Article R. Ramakrishna, Berhe Aregay, Tewodros Gebregergs. The Comparison in Time Series Forecasting of Air Traffic Data by Autoregressive Integrated Moving Average Model, Radial Basis Function and Elman Recurrent Neural Networks. Research & Reviews: Journal of Statistics . 2018; 7(3): 75–90p.

Oilseeds have been the backbone of India’s agricultural economy since long. Oilseed crops play the second most important role in Indian agricultural economy, next to food grains, in terms of area and production. Oilseeds production in India has increased with time, however, the increasing demand for edible oils necessitated the imports in large quantities, leading to a substantial drain of foreign exchange. The need for addressing this deficit motivated a systematic study of the oilseeds economy to formulate appropriate strategies to bridge the demand-supply gap. In this study, an effort is made to forecast oilseeds production by using Autoregressive Integrated Moving Average (ARIMA) model, which is the most widely used model for forecasting time series. One of the main drawbacks of this model is the presumption of linearity. The Group Method of Data Handling (GMDH) model has also been applied for forecasting the oilseeds production because it contains nonlinear patterns. Both ARIMA and GMDH are mathematical models well-known for time series forecasting. The results obtained by the GMDH are compared with the results of ARIMA model. The comparison of modeling results shows that the GMDH model perform better than the ARIMA model in terms of mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The experimental results of both models indicate that the GMDH model is a powerful tool to handle the time series data and it provides a promising technique in time series forecasting methods.

Forecasting oil, coal, and natural gas prices in the pre-and post-COVID scenarios: Contextual evidence from India using time series forecasting tools

Inflation is one of the indicators to maintain economic stability. Controlling inflation reflects the success of economic growth, while very high or volatile inflation can lead to economic instability. The purpose of this research is to forecast the time series data of inflation rate in Indonesia until the end of 2024 using ARIMA method. The data used in this study are secondary data of monthly inflation rates in Indonesia from January 2003 to May 2024 obtained from the Bank Indonesia website. Based on the research results, the optimal model for forecasting the inflation rate in Indonesia until the end of 2024 is ARIMA (1,0,1) with a MAPE of 6.91%. The forecasting results show a stable and not too significant increase and are still within the target range set by Bank Indonesia and the Government, which is between 1,5% and 3,5% for 2024.

The article examines the independent application of ARIMA (Auto Regressive Integrated Moving Average) and SVM (Support Vector Machine) methods for forecasting non-scheduled passenger air transportation. Based on the SVM model, the data is classified based on different kernel functions, and the best prediction results are determined. The autoregression (ARIMA) model is applied to identify linear trends and regularities within time series data. Based on the analysis, the results show that ARIMA and SVM models offer superior forecasting accuracy and reliability. Nevertheless, the relative error of the prediction results compared to the actual indicators is smaller in the SVM model. This also shows that the identification of non-linear relationships between data in non-scheduled passenger air transportation makes forecasting results more effective and optimal. The obtained results will serve as an effective tool in forecasting the demand for non-scheduled passenger air transportation. As a result, substantial support will be observed in the planning of operations in the mentioned field, preparation of the existing infrastructure according to the demand, etc.

Chaos and reproduction in sea level