Consumer Price Index (CPI) is one of the economic indicators used to measure inflation. Badan Pusat Statistik (BPS) publishes a monthly CPI and inflation with a time lag of one working day. Policies based on the monthly inflation rate could be losing momentum as the events associated with inflation had occurred long before inflation or CPI was published. Therefore, it is necessary to calculate daily CPI to describe near real-time price changes. Nowcasting can overcome this issue by predicting daily inflation through predicting daily CPI. The calculation of daily CPI is done by entering daily data price of basic commodities in Sistem Informasi Ketersediaan dan Perkembangan Harga Bahan Pokok (SISKAPERBAPO), daily Jakarta Interbank Spot Dollar Rate (JISDOR) from Bank Indonesia, and daily Brent crude oil futures prices from Id Investing into a nowcasting model and validated by monthly CPI published by BPS. The nowcasting method used in this study is the Time Series Regression (TSR) and Support Vector Regression (SVR) applied to predict daily CPI nowcasts in East Java Province. The performance comparison between TSR and SVR is evaluated based on the Root Mean Square Error (RMSE), symmetric Mean Absolute Percentage Error (sMAPE), and Mean Absolute Deviation (MAD). SVR-Polynomial is the best method for predicting daily CPI.

Topics
Crude oil, Java, Telecommunications engineering, News and events, Regression analysis, Descriptive statistics
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