Predicting daily consumer price index using support vector regression method based cloud computing

Severe inflation can cause a country's economic downturn. Therefore, inflation needs to be controlled. One of inflation control conducted by the government is predicting and calculating inflation using CPI indicators on a monthly. Prediction with monthly frequency, could be too late, because inflation has been a few days and it is not known quickly. With the development of internet technology today, various data sources related to inflation easily obtained in real-time. This data can be used for daily CPI prediction. Daily predictions allow policy makers to make better policies. CPI prediction using daily data will face challenges. The growing variants and data volumes need good computing systems. Cloud computing can be used to solve the problem. This is a preliminary research in developing daily CPI prediction model using big data and cloud computing. Here we focus on developing a daily CPI prediction model using the Support Vector Regression (SVR) method in a cloud computing. For better accuracy, we compared the kernel functions of SVR and tuning SVR parameters using the grid search and Random Search method. In addition, we compared SVR with the Random Forest method. These daily CPI predictions are simulated into cloud computing environments. From this simulation we show computation time and accuration comparisons needed if run on personal computers with cloud computing. The results showed that SVR using RBF kernel has less mse value 0.3454 in monthly prediction and 0.0095 in daily predictions. And Random Forest result is slightly different than SVR — RBF, mse value 0.0171 in daily prediction. Experiment show that running CPI prediction have less time, for 1644 data need takes 522s than PC takes 837s.