Forecasting The Consumer Price Index: A Comparative Study of Machine Learning Methods

Abstract

The Consumer Price Index (CPI) is an indicator of inflation and is tracked by many government and economic agencies to make decisions of major importance. Its prediction is a valuable input into government policies such as taxation, and it greatly impacts the cost of borrowing money. The CPI has been traditionally predicted with statistical methods such as the Autoregressive Integrated Moving Average (ARIMA) model. In this paper, we forecast the Saudi Arabian Consumer Price Index with six machine learning (ML) methods, using the Orange 3 data mining and analytics tool, and based on the published historical January 2013 to November 2020 CPI data. We compare the performances of Decision Tree (Tree), k-Nearest Neighbors (kNN), Linear Regression (LR), Neural Networks (NN), Random Forest (RF), and Support Vector Machine (SVM), all applied to the 2013-2020 Saudi Arabian CPI dataset. Multiple experiments were conducted to vary the training and testing sets, optimize the machine learning parameters, and improve the MSE and R2 metrics. The predicted CPI values of these ML methods were also compared to the 2021-2024 International Monetary Fund (IMF) CPI forecast and the actual 2021-2024 CPIs (post mortem). The results indicate that the multilayer perceptron neural network model outperforms the other ML models, is nearest to the actual CPI, and may be used to forecast the CPI for up to 3 years from the latest CPI data in the training dataset. The kNN model follows the neural network model in second place. The best fitting Excel trend line underperformed all ML methods in forecasting the Saudi Arabian CPI.