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All Journal Indonesian Journal of Statistics and Its Applications
Suhartono Suhartono
Department of Statistics, Institut Teknologi Sepuluh November, Indonesia
Computer Science & IT Mathematics Other

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Comparison of Short-Term Load Forecasting Based on Kalimantan Data Syalam Ali Wira Dinata; Muhammad Azka; Primadina Hasanah; Suhartono Suhartono; Moh Danil Hendry Gamal
Indonesian Journal of Statistics and Applications Vol 5 No 2 (2021)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v5i2p243-259

Abstract

This paper investigates a case study on short term forecasting for East Kalimantan, with emphasis on special days, such as public holidays. A time series of load demand electricity recorded at hourly intervals contains more than one seasonal pattern. There is a great attraction in using a modelling time series method that is able to capture triple seasonalities. The Triple SARIMA model has been adapted for this purpose and competitive for modelling load. Using the least squares method to estimate the coefficients in a triple SARIMA model, followed by model building, model assumptions and comparing model criteria, we propose and demonstration the triple Seasonal Autoregressive Integrated Moving Average model with AIC 290631.9 and SBC 290674.2 as the best model for this study. The Triple seasonal ARIMA is one of the alternative strategy to propose accurate forecasts of electricity load Kalimantan data for planning, operation maintenance and market related activities.
Forecasting Currency in East Java: Classical Time Series vs. Machine Learning J A Putri; Suhartono Suhartono; H Prabowo; N A Salehah; D D Prastyo; Setiawan Setiawan
Indonesian Journal of Statistics and Applications Vol 5 No 2 (2021)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v5i2p284-303

Abstract

Most research about the inflow and outflow currency in Indonesia showed that these data contained both linear and nonlinear patterns with calendar variation effect. The goal of this research is to propose a hybrid model by combining ARIMAX and Deep Neural Network (DNN), known as hybrid ARIMAX-DNN, for improving the forecast accuracy in the currency prediction in East Java, Indonesia. ARIMAX is class of classical time series models that could accurately handle linear pattern and calendar variation effect. Whereas, DNN is known as a machine learning method that powerful to tackle a nonlinear pattern. Data about 32 denominations of inflow and outflow currency in East Java are used as case studies. The best model was selected based on the smallest value of RMSE and sMAPE at the testing dataset. The results showed that the hybrid ARIMAX-DNN model improved the forecast accuracy and outperformed the individual models, both ARIMAX and DNN, at 26 denominations of inflow and outflow currency. Hence, it can be concluded that hybrid classical time series and machine learning methods tend to yield more accurate forecasts than individual models, both classical time series and machine learning methods.
Comparison of Short-Term Load Forecasting Based on Kalimantan Data Syalam Ali Wira Dinata; Muhammad Azka; Primadina Hasanah; Suhartono Suhartono; Moh Danil Hendry Gamal
Indonesian Journal of Statistics and Applications Vol 5 No 2 (2021)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v5i2p243-259

Abstract

This paper investigates a case study on short term forecasting for East Kalimantan, with emphasis on special days, such as public holidays. A time series of load demand electricity recorded at hourly intervals contains more than one seasonal pattern. There is a great attraction in using a modelling time series method that is able to capture triple seasonalities. The Triple SARIMA model has been adapted for this purpose and competitive for modelling load. Using the least squares method to estimate the coefficients in a triple SARIMA model, followed by model building, model assumptions and comparing model criteria, we propose and demonstration the triple Seasonal Autoregressive Integrated Moving Average model with AIC 290631.9 and SBC 290674.2 as the best model for this study. The Triple seasonal ARIMA is one of the alternative strategy to propose accurate forecasts of electricity load Kalimantan data for planning, operation maintenance and market related activities.
Forecasting Currency in East Java: Classical Time Series vs. Machine Learning J A Putri; Suhartono Suhartono; H Prabowo; N A Salehah; D D Prastyo; Setiawan Setiawan
Indonesian Journal of Statistics and Applications Vol 5 No 2 (2021)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v5i2p284-303

Abstract

Most research about the inflow and outflow currency in Indonesia showed that these data contained both linear and nonlinear patterns with calendar variation effect. The goal of this research is to propose a hybrid model by combining ARIMAX and Deep Neural Network (DNN), known as hybrid ARIMAX-DNN, for improving the forecast accuracy in the currency prediction in East Java, Indonesia. ARIMAX is class of classical time series models that could accurately handle linear pattern and calendar variation effect. Whereas, DNN is known as a machine learning method that powerful to tackle a nonlinear pattern. Data about 32 denominations of inflow and outflow currency in East Java are used as case studies. The best model was selected based on the smallest value of RMSE and sMAPE at the testing dataset. The results showed that the hybrid ARIMAX-DNN model improved the forecast accuracy and outperformed the individual models, both ARIMAX and DNN, at 26 denominations of inflow and outflow currency. Hence, it can be concluded that hybrid classical time series and machine learning methods tend to yield more accurate forecasts than individual models, both classical time series and machine learning methods.
Co-Authors D D Prastyo H Prabowo J A Putri Moh Danil Hendry Gamal Muhammad Azka N A Salehah Primadina Hasanah Setiawan Setiawan Syalam Ali Wira Dinata