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S Suhartono
Institut Teknologi Sepuluh Nopember
Computer Science & IT Engineering Medicine & Pharmacology Social Sciences Other

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Hybrid ARIMAX-NN Model for Forecasting Inflation Santi Eksiandayani; S Suhartono; Dedy Dwi Prastyo
Proceeding ISETH (International Summit on Science, Technology, and Humanity) 2015: Proceeding ISETH (International Conference on Science, Technology, and Humanity)
Publisher : Universitas Muhammadiyah Surakarta

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Abstract

Inflation became an important component in the economy as an indicator of the increase in prices of goods and services. In addition to general inflation, there are also seven groups of inflation categorized based on expenditure. Inflation particularly in Indonesia is influenced by internal and external factors. These factors may effect inflation not only at a single point of time, but also at certain periods. Money supply is one factor strongly considered to influence inflation. Consequently, it is important to forecast inflation by involving money supply as input series. The effect of money supply on inflation was analyzed in this study. This research focused on hybrid method which is the combination between Autoregressive Integrated Moving Average with Exogenous Factor (ARIMAX) and Neural Network (NN). The resultsof hybrid method were compared to individual forecasting method, i.e. ARIMA and ARIMAX. The result indicated that hybrid ARIMAX-NN provided precise inflation prediction compared to ARIMA or ARIMAX method. Hybrid model can be an effective and efficient way to improve forecasting.
Modeling Inflation Volatility Using ARIMAX-GARCH Sri Aryani; Heri Kuswanto; S Suhartono
Proceeding ISETH (International Summit on Science, Technology, and Humanity) 2015: Proceeding ISETH (International Conference on Science, Technology, and Humanity)
Publisher : Universitas Muhammadiyah Surakarta

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Abstract

Forecasting inflation is necessary as a basis for making decisions and high quality good planning in economic development in Indonesia particularly for the government and businessmen. The forecasting generally uses time series data. However, there is a time series data which is difficult to obtain stationary, i.e., the variance on financial time series data such as the stock price index, interest rates, inflation, exchange rates, and etc. It is mainly caused by the inconsistency of variance (heteroscedasticity). This study developed Autoregressive Integrated Moving Average (ARIMA) model using exogenous factors, namely the price of oil and outlier detection to forecast inflation. Another modeling which is expected to solve the problem of heteroscedasticity is a Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model. In this study, the asymmetric GARCH of Glosten Jagannathan Runkle-GARCH (GJR-GARCH) was carried out. This model could accommodate the volatility in the form of negative shocks that can leverage the effect. The data used in this study was the Inflation rate of Indonesia and world oil prices in January 1991 to December 2014 respectively. The results showed that ARIMAX-GJR GARCH is the best model to forecast national inflation volatility.
Co-Authors Dedy Dwi Prastyo Heri Kuswanto Santi Eksiandayani Sri Aryani