Comparison Of Support Vector Regression And Autoregressive Integrated Moving Average With Exogenous Variable On Indonesia Consumer Price Index
Comparison Of Support Vector Regression And Autoregressive Integrated Moving Average With Exogenous Variable On Indonesia Consumer Price Index

Summary/Abstract: CPI is one of the most frequently used indicators to measure the inflation rate in a region. The government can maintain economic stability by knowing the CPI value in advance. Therefore, we need a suitable method to predict an accurate CPI value. In this research, we investigate the prediction of CPI based on the machine learning method, SVR, and compare it to the ARIMAX method. We use Indonesia CPI data from January 2015 to October 2021. We investigate the SVR method using four kernel functions: Radial Basis Function (RBF), Polynomial, Linear, and Sigmoid. We build the ARIMAX model through the auto ARIMA process. We divide the data into two parts with three scenarios to investigate the performance of the methods: training and testing. The results show that the partition of 80% training and 20% testing gives the best performance. The SVR method performs best using a linear kernel, with an RMSE value of 0,743 and a MAPE value of 0,684%. The best ARIMAX model is model (0,2,1) with an RMSE value of 1,928071 and a MAPE of 1,731598 %. From the plot of prediction results and indicators of RMSE and MAPE, the SVR predicts CPI data better than the ARIMAX method, with CPI in the previous one-month data (MA1) being the most influential variable on the next CPI value.

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