📈 Forecasting the Consumer Price Index of Malaysia

A Comparative Study of Machine & Deep Learning Approaches

Accurate forecasting of the Consumer Price Index (CPI) is significant for informed economic planning and decision-making. This project compares the performance of four machine and deep learning models in predicting Malaysia's CPI using various input structures.

🧠 Models Compared

Support Vector Regression (SVR)
Random Forest (RF)
XGBoost
Neural Networks (NN)

📊 Input Structures

Univariate Input: CPI only
Multivariate Input: 12 economic indicators
Simple Multivariate Input: Top 3 most correlated indicators

🔍 Key Findings

SVR consistently outperformed other models across all input settings, particularly when using 12 multivariate indicators.
In the multivariate setting, SVR achieved the best metrics:
- MAE: 0.0584
- RMSE: 0.0710
- R²: 0.99997
Random Forest (RF) also showed strong performance in univariate and simplified multivariate configurations.
Neural Networks (NN) demonstrated the weakest performance, particularly in multivariate settings (MAE up to 1.05).

🌏 Regional Adaptability

SVR was applied to CPI datasets from Cambodia, Myanmar, and Laos to test cross-country performance.
It maintained:

R² > 0.99
MAE < 0.2

These results highlight SVR’s robustness across Southeast Asian economies.

🛠️ Tools & Libraries Used

Python, Jupyter Notebook
pandas, numpy, seaborn, matplotlib
scikit-learn, XGBoost, Keras
statsmodels, scikit-plot

📁 Repository Structure

FYP-CPI-Forecasting

data/ # Datasets (Malaysia, Cambodia, Laos, Myanmar)
notebooks/ # Model development and testing
results/ # Visualizations and metrics
README.md # Project documentation

📌 Conclusion

This study demonstrates that machine learning—particularly Support Vector Regression (SVR) is highly effective in forecasting CPI, outperforming deep learning in both accuracy and consistency. The model's success across neighboring countries suggests its broader regional applicability.

Leyan0109/FYP_Malaysia-CPI-Forecasting