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.