PERAMALAN PENGGUNAAN LISTRIK DI PROVINSI BALI MENGGUNAKAN METODE ARIMA

Inflation is one of the key indicators that reflects the economic stability of a region. Inflation instability can directly impact the purchasing power of the population, increase poverty rates, and create imbalances in macroeconomic policies. In Lampung Province, inflation fluctuations have become a significant issue requiring attention, particularly in the context of regional economic planning and policy-making. This study forecasts the inflation rate using the Autoregressive Integrated Moving Average (ARIMA) method, which is known to be effective in analyzing time series data and providing accurate short-term estimates. The data used comprises monthly inflation rates from 2006 to 2023, obtained from the Central Bureau of Statistics (BPS) of Lampung Province. Five ARIMA model configurations were tested: ARIMA(3,1,2), ARIMA(3,1,1), ARIMA(2,1,1), ARIMA(1,1,1), and ARIMA(5,1,1). Based on the evaluation of the Akaike Information Criterion (AIC) and Mean Absolute Percentage Error (MAPE), the ARIMA(1,1,1) model was identified as the best-performing model, with the lowest AIC value and a MAPE of 0.57. The model also passed diagnostic tests, including residual normality and white noise assessment using the Ljung-Box test. The forecasting results indicate a gradual upward trend in inflation, with predicted rates of 0.23% in January 2024, 0.29% in February 2024, and 0.30% in March 2024. These findings provide early indications that inflation in Lampung Province tends to increase in the short term, and can serve as a basis for formulating more targeted regional inflation control policies.

Objective: The objective of this study was to forecast the number of critical days, for the period from July 2020 to December 2022, based on time series analysis, to model changes in the number of critical power days in a prospective way. Related Studies: This section provides an overview of the use of forecasting models and methods for measuring electricity demand, applied as decision-making support and as a tool for identifying anomalous phenomena. Method: The methodology for this research involves analysis and forecasting using the Autoregressive Integrated Moving Average (ARIMA) model of order (p,d,q)(p,d,q), applied to the time series of critical days. Model development and performance evaluation followed these steps and strategies: Specification (Pre-processing), Identification and Estimation, Verification, and Forecasting. Data collection was performed by analyzing time series observations from a Brazilian electric utility company responsible for energy supply in Rio Grande do Sul. Results and Discussion: The results indicated that the seasonal ARIMA (2,1,1)(2,1,1) model performed best, with the lowest Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values, as well as the lowest mean square error among the tested models. With the selected ARIMA model, forecasts were made for the following 30 months, estimating the number of monthly critical days. Research Implications: The paper discusses the practical and theoretical implications of forecasting critical days for power quality. Practically, it presents an ARIMA model to estimate these days, aiding power companies in projecting interruptions and improving preventive management and resource allocation. Theoretically, it contributes to the time series forecasting literature in the power sector, confirming the effectiveness of ARIMA models with seasonal components for grid management and adverse event forecasting in complex systems. Originality/Value: The study contributes to the literature by applying the ARIMA model with seasonal components to predict critical days of interruption in the distribution of electricity, an approach that has not yet been explored for specific adverse events. The originality lies in the application of the model in a context of quality and reliability in the supply and detailed seasonal analysis. The methodology proposes a solution for forecasting and managing these events, bringing new perspectives for proactive management in the sector. The study stands out for its potential to improve professional practice, allowing informed decisions for resource allocation and maintenance, increasing reliability and reducing costs and financial compensation.

Tuberculosis is a disease that can affect socio-economic development. Based on data from the World Health Organization, there were 810,918 tuberculosis cases in Indonesia, which is noted as the third-highest number of tuberculosis cases in Asia in 2016. Prevention and control of tuberculosis are of considerable importance, especially in the insurance field, to cover the cost of treatment, so an accurate model of tuberculosis morbidity is needed. The method used in forecasting the tuberculosis morbidity rate is Autoregressive Integrated Moving Average (ARIMA) method. The ARIMA method is a time series method that is widely used to predict morbidity rates in the future. The data used in this study is the number of incidence morbidity tuberculosis rates that occurred in Indonesia from 2000 to 2017, which is obtained from the World Bank. The results showed that ARIMA (1, 2, 0) is the best and very accurate model to forecast the morbidity rate in Indonesia from 2018 to 2027, with the mean absolute percentage error (MAPE) is 0.1682 % and Akaike Information Criterion (AIC) values is -181.0120. The results of forecasting tuberculosis morbidity rate are expected to help insurance companies in determining the amount of premium paid by customers who suffer tuberculosis diseases.

BackgroundChina is a country that is most seriously affected by hemorrhagic fever with renal syndrome (HFRS) with 90% of HFRS cases reported globally. At present, HFRS is getting worse with increasing cases and natural foci in China. Therefore, there is an urgent need for monitoring and predicting HFRS incidence to make the control of HFRS more effective. In this study, we applied a stochastic autoregressive integrated moving average (ARIMA) model with the objective of monitoring and short-term forecasting HFRS incidence in China.MethodsChinese HFRS data from 1975 to 2008 were used to fit ARIMA model. Akaike Information Criterion (AIC) and Ljung-Box test were used to evaluate the constructed models. Subsequently, the fitted ARIMA model was applied to obtain the fitted HFRS incidence from 1978 to 2008 and contrast with corresponding observed values. To assess the validity of the proposed model, the mean absolute percentage error (MAPE) between the observed and fitted HFRS incidence (1978-2008) was calculated. Finally, the fitted ARIMA model was used to forecast the incidence of HFRS of the years 2009 to 2011. All analyses were performed using SAS9.1 with a significant level of p < 0.05.ResultsThe goodness-of-fit test of the optimum ARIMA (0,3,1) model showed non-significant autocorrelations in the residuals of the model (Ljung-Box Q statistic = 5.95,P = 0.3113). The fitted values made by ARIMA (0,3,1) model for years 1978-2008 closely followed the observed values for the same years, with a mean absolute percentage error (MAPE) of 12.20%. The forecast values from 2009 to 2011 were 0.69, 0.86, and 1.21per 100,000 population, respectively.ConclusionARIMA models applied to historical HFRS incidence data are an important tool for HFRS surveillance in China. This study shows that accurate forecasting of the HFRS incidence is possible using an ARIMA model. If predicted values from this study are accurate, China can expect a rise in HFRS incidence.

Background Breast cancer represents a significant public health concern in India, accounting for 28% of all cancer diagnoses and imposing a substantial economic burden. This study introduces a novel approach to forecasting the number of breast cancer cases (based on prevalence rates) and estimating the associated economic impact in India using the autoregressive integrated moving average (ARIMA) model. Methods Data on the prevalence of breast cancer in India from 2000 to 2021 were obtained from the Global Burden of Disease (GBD) database. This dataset provided annual estimates of the number of patients with breast cancer, serving as the basis for modeling future prevalence and estimating the economic burden. The ARIMA (Auto-Regressive Integrated Moving Average) model was employed to analyze and predict breast cancer prevalence in India up to the year 2030 (time-series forecasting). Data were visualized and checked for stationarity using the Augmented Dickey-Fuller (ADF) test. Using the autocorrelation function (ACF) and partial autocorrelation function (PACF) plots, the appropriate parameters (p, d, q) were determined. Several ARIMA configurations were tested to identify the model with the best fit. The goodness-of-fit of the model was assessed using standard metrics such as the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The residuals were tested using the Box-Ljung test to confirm the absence of autocorrelation and verify that they followed a white noise distribution. Using the fitted ARIMA model, prevalence rates were forecasted from 2022 to 2030, with 95% confidence intervals to capture prediction uncertainty. Direct costs were calculated based on medical expenses for breast cancer patients, such as hospital visits, diagnostic tests, treatment costs, and follow-up care. A bottom-up approach was applied, which involves aggregating individual cost components from each stage of care to estimate the total direct burden of disease. A bottom-up approach was applied, which involves aggregating individual cost components from each stage of care to estimate the total direct burden of disease. Indirect costs were estimated using the human capital approach, which assesses productivity losses due to morbidity and premature mortality. The Disability-Adjusted Life Years (DALY) associated with breast cancer were also predicted using the ARIMA model. Results The results of coefficient of determination (0.99), mean absolute percentage error (69%), mean absolute error (5229), and root mean squared error (6451.2) showed that the ARIMA (0,2,0) model fitted well. Coefficient of determination (0.99) indicated that 99% of the variance in the data was explained by the model. Akaike information criterion (411.54) and Bayesian information criterion (412.53) indicated the ARIMA (0,2,0) model was reliable when analysing our data. The result of the relative error of prediction (2.76%) also suggested that the model predicted well. The number of patients with breast cancer from 2021 to 2030 was predicted to be about 1.25 million, 1.1.29 million,, 1.34 million, 1.39 million, 1.44 million, 1.48 million, 1.53 million, 1.58 million, 1.63 million, 1.68 million, and respectively. The total economic burden of breast cancer from 2021 to 2030 was estimated to be $8 billion, $8.72 billion, $9.05 billion, $9.84 billion, $10.20 billion, $11.07 billion, $11.49 billion, $12.44 billion, $12.91 billion, $13.95 billion, respectively is estimated to rise significantly. Conclusion Breast cancer prevalence and its economic impact are projected to grow substantially in India. Between 2021 and 2030, the number of breast cancer patients is expected to increase by approximately 0.05 million annually, with an annual increase rate of about 5.6%. The associated economic burden will also rise, averaging an additional $19.55 billion per year, underscoring the need for intensified healthcare and economic strategies to manage this growing challenge.

The Philippines is one of the fastest urbanizing countries in the East Asia and Pacific region (Baker &amp; Watanabe, 2017). Despite having its advantages, urbanization still has its challenges that require extensive urban management and development programs for it to be prevented and minimized. In this paper, the researchers forecasted the urban population growth of the Philippines using the Autoregressive Integrated Moving Average (ARIMA) Model. The historical data obtained from the World Bank Group was from 1960 to 2020. The R Programming Language was used as the medium for the entire forecasting process. Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plots, Augmented Dickey-Fuller (ADF) test, Phillips-Perron (PP) test, and Kwiatkowski, Phillips, Schmidt, and Shin (KPSS) test were used for testing the stationarity of the time-series data. Moreover, Akaike Information Criteria (AIC), Corrected Akaike Information Criterion (AICc), and Schwarz Information Criteria (SIC) were used as criteria for selecting the best ARIMA model. It was shown that the best ARIMA model for forecasting the urban population growth of the country is ARIMA (20, 1, 10). This model has been formulated and chosen through the mentioned statistical tests, and criteria for validation, and was further validated using error measures. The chosen ARIMA model was proven to be accurate based on the Root Mean Square Error (RMSE) of 0.18877 and the Mean Absolute Percentage Error (MAPE) of 3.71%. The researchers found an increase in the trend of 1.95% by 2022, 2.08% by 2024, 2.19% by 2026, and 2.36% by 2028. This potential rise in urban population growth in the Philippines may improve the economy of the country for the next 6 years, but this could also imply that the underlying issues of urbanization may get worse. The researchers conclude that the Philippine national government and local government units should have better and strengthened urban management and development programs to aid these problems. Government officials and even private sectors may use this paper as a reference to have an informed decision and policy-making. KEYWORDS: Autoregressive Integrated Moving Average (ARIMA) Model, Box-Jenkins Method, Urbanization, Urban Population Growth, Forecast, R Programming Language

Background:Visceral leishmaniasis in human (VLH) also known as kala-azar is a neglected disease of humans that mainly occurs in more than 50 countries mostly located in the Eastern Mediterranean and the Northern America.Objective:The purpose of this study was to determine the temporal patterns and predict of occurrence of VL in Ardabil Province, in northwestern Iran using autoregressive integrated moving average (ARIMA) models.Methods:This descriptive study employed yearly and monthly data of 602 cases of VLH in the province between January 2000 to December 2019, which was provided by the leishmaniasis national surveillance system. The monthly occurrences case constructed the ARIMA model of time-series model. The insignificance of the correlation in the lags of 12, 24 and 36 months, and Chi-square test showed the occurrence of VLH does not have a seasonal pattern. Eleven potential ARIMA models were examined for VLH cases. Finally, the best model was selected with the lower Akaike Information Criteria (AIC) and Bayesian information criterion (BIC) value. Then, the selected model was used to forecast frequency of monthly occurrences case. The forecasting precision was estimated by mean absolute percentage error (MAPE). Data analysis was performed using Stata14 and its package time series analysis.Results:ARIMA (5, 0, 1) model with AIC (25.7) and BIC (43.35) was selected. The MAPE value was 26.89% and the portmanteau test for white noise was (Q = 23.02, P = 0.98) for the residuals of the selected model showed that the data were fully modelled. The total cumulative VLH cases in the next 24 months’ in Ardabil province predicted 14 cases (95% CI: 4-54 case).Conclusion:The ARIMA (5, 0, 1) model can be a useful tool to predict VLH cases as early warning system and the results are helpful for policy makers and primary care physicians in the readiness of public health problems before the outbreak of the disease.

This paper attempts to forecast the economic performance of Bangladesh measured with annual GDP data using an Autoregressive Integrated Moving Average (ARIMA) Model followed by test of goodness of fit using AIC (Akaike Information Criterion) and BIC (Bayesian Information Criterion) index value among six ARIMA models along with several diagnostic tests such as plotting ACF (Autocorrelation Function), PACF (Partial Autocorrelation Function) and performing Unit Root Test of the Residuals estimated by the selected forecasting ARIMA model. We have found the appropriate ARIMA (1,0,1) model useful in predicting the GDP growth of Bangladesh for next couple of years adopting Box-Jenkins approach to construct the ARIMA (p,r,q) model using the GDP data of Bangladesh provided in the World Bank Data stream from 1961 to 2019. JEL classification numbers: B22, B23, C53. Keywords: GDP growth, ACF, PACF, Stationary, ARIMA (p,r,q) model, Forecasting.

The state of Selangor, in Malaysia consist of urban and peri-urban centres with good transportation system, and suitable temperature levels with high precipitations and humidity which make the state ideal for high number of dengue cases, annually. This study investigates if districts within the Selangor state do influence each other in determining pattern of dengue cases. Study compares two different models; the Autoregressive Integrated Moving Average (ARIMA) and Ensemble ARIMA models, using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) measurement to gauge their performance tools. ARIMA model is developed using the epidemiological data of dengue cases, whereas ensemble ARIMA incorporates the neighbouring regions’ dengue models as the exogenous variable (X), into traditional ARIMA model. Ensemble ARIMA models have better model fit compared to the basic ARIMA models by incorporating neighbuoring effects of seven districts which made of state of Selangor. The AIC and BIC values of ensemble ARIMA models to be smaller compared to traditional ARIMA counterpart models. Thus, study concludes that pattern of dengue cases for a district is subject to spatial effects of its neighbouring districts and number of dengue cases in the surrounding areas.

Inflation is an economic event that often occurs even though it is not wanted. Based on data from Badan Pusat Statistik in 2015-2020 inflation in East Java was 3.08%, 2.74%, 4.04%, 2.86%, 2.12%, 1.44%. From these data, it can be seen that inflation data is fluctuating. Therefore it is necessary to control inflation because high and unstable inflation can have a negative impact on the socio-economic conditions of the community. In addition, it also makes it difficult for the government to determine future policies. Seeing the importance of controlling inflation, it is necessary to study to predict the inflation rate in the future. One of the studies/methods to predict that is often used is the Autoregressive Integrated Moving Average (ARIMA) method or also known as the Box-Jenkins method. The ARIMA method is a method that is easy to use because it is flexible in following existing data patterns and has high accuracy and tends to have a small error value because of the detailed process. From the analysis results, the best ARIMA (p,d,q) model is the ARIMA model (2,1,1) with an AIC value of 76.77. The results of forecasting with the ARIMA model (2,1,1) respectively are 0.2593698, 0.1892990, 0.1340639, 0.1368309, 0.1572021, 0.1642381, 0.1598897, 0.1557251, 0.1556074, 0.1570151, 0.1576092, 0.1573511, 0.1570423, and 0.1570111.

There is a lot of entrepreneurial competition in the production of goods or services in the world, especially in Indonesia, especially the production of staple goods, namely sugar. The problem that is often faced at Sugar Factory PTPN XI Semboro Jember is the lack of management that is neatly organized and efficient, which makes this company less working optimally. Often there is a lack and excess of sugar production which makes the sugar does not have the maximum value, the sugar has been damaged, and sales at a reduced price because the sugar is not as efficient as the initial product. From these various problems, it can reduce profits from the company. From these problems it can be concluded that the company needs a system that can organize the management of the company, and is able to forecast production in the future. In this research will make a forecasting system using the method of Autoregressive Integrated Moving Average (ARIMA), where this method is divided into three methods, namely the Autoregressive (AR) method, the Moving Average (MA) method, and the Autoregressive Integrated Moving Average (ARIMA) method, which preceded by checking stationary data, and modeling the Autoregressive Integrated Moving Average (ARIMA) method. Forecasting is done using production data for the previous 12 years from the company. The system is made to facilitate management that is less organized and displays predictions for the next production period. The results of this forecasting system are to determine the amount of production each year needed in this company. From the results of the ARIMA method modeling, the right ARIMA method is obtained by the ARIMA / AR (1,0,0), ARIMA / MA (0,0,1), and ARIMA (1,0,1) methods. The test results found that the average value of Mean Absolute Percentage Error (MAPE) in the Autoregressive (AR) method was 17%, the Moving Average (MA) method was 19%, and the Autoregressive Integrated Moving Average (ARIMA) method was 15%.

Forecasting plays a pivotal role in economic planning, particularly in aligning supply with demand and informing production decisions. This study aims to compare the performance of the Autoregressive Integrated Moving Average (ARIMA) and Seasonal ARIMA (SARIMA) models in forecasting the non-oil and gas export values of East Java, a region known for its dynamic trade activity. Using monthly time series data spanning from January 2007 to January 2024, sourced from the Central Statistics Agency (BPS) of East Java Province, this research conducts an in-depth analysis of forecasting accuracy and model suitability. Before model implementation, the dataset underwent several preprocessing steps to ensure its quality, including the handling of missing values and outlier adjustments. Both ARIMA and SARIMA models were developed, calibrated, and evaluated using standard forecasting performance metrics, namely Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The ARIMA model exhibited consistently lower error rates across all three metrics, indicating its robustness in capturing the underlying patterns within the export data. In contrast, while the SARIMA model incorporated seasonal components, its performance did not surpass that of ARIMA in this specific case. The comparative findings suggest that, despite the seasonal nature of trade, the ARIMA model is more suitable for short-term forecasting of East Java’s non-oil and gas exports. This research contributes to the broader literature on economic forecasting by emphasizing the importance of selecting appropriate models based on data characteristics. Furthermore, the results provide valuable insights for policymakers and stakeholders engaged in export planning and regional trade development In this result the ARIMA model overcome the SARIMA with MAPE 0.116 to 0.983.

Background: COVID-19 has claimed the lives of millions of people in Nigeria and around the world during the last two years. It is a recognized global health crisis of our day, as well as a persistent threat to the earth. The goal of this study was to examine the trend and fit an Error Trend and Seasonal (ETS) exponential smoothing and Autoregressive Integrated Moving Average (ARIMA) model to Nigeria's COVID-19 daily fatalities.Methods: A dataset of daily COVID-19 confirmed fatality cases was used in the investigation. Data was acquired from the Nigerian Centre for Disease Control (NCDC) web database between the 10th of July 2020 and the 2nd of December 2021. The ARIMA model and twelve (12) ETS exponential smoothing techniques were investigated using a dataset of COVID-19 pandemic deaths in Nigeria. The ARIMA and ETS exponential smoothing algorithms were evaluated using the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Hannan Quinn Information Criterion (HQC), and Average Mean Squared Error (AMSE) selection criteria.Result: The ARIMA (0,1,0) model was the best time series modeling for the coronavirus (COVID-19) epidemic in Nigeria since it had the lowest AIC=2863.51, BIC=2866.90, HQ = 2866.90, and AMSE = 0.55471 values.Conclusion: The ARIMA (0,1,0) model is preferred above the other thirteen (13) competing models based on daily confirmed COVID-19 deaths in Nigeria. This research would assist the Nigerian government in better understanding the pestilence's evolution pattern and providing adequate provisions, prompt mediation, and treatment to prevent additional deaths caused by the virus.

The purpose of this paper is to predict the lending interest rate and deposit interest rate of Bangladesh using the Autoregressive Integrated Moving Average (ARIMA) model by Box Jenkins. It has been found that ARIMA (1, 0, 1) model is appropriate in predicting both the lending and deposit interest rates from 2022 to 2026 using the data presented in the World Bank Open Data from 1976 to 2021. To test the goodness of fit, AIC (Akaike Information Criterion) and BIC (Bayesian Information Criterion) index values have been calculated for six ARIMA models. Besides, Dickey-Fuller unit root test and Correlogram test have also been conducted for diagnostic tests.

PIN79 VACCINATION COVERAGE RATE: WHAT CAN WE EXPECT FROM BRAZIL?