Abstract:
This study investigates the effectiveness of feature engineering in enhancing the accuracy of
multivariate time series forecasting, addressing limitations of conventional statistical and machine
learning models. Two U.S. housing-related datasets were analysed using regression, machine
learning, and deep learning approaches. Feature engineering techniques, including lag,
rolling window, expanding window, and seasonality, were applied, and feature importance was
validated using Random Forest, XGBoost, and Decision Tree classifiers. Forecasting was performed
with regression models, VAR, and LSTM, while Root Mean Square Error (RMSE) was
employed to validate and compare model performance. Results demonstrate that feature engineering
significantly improves forecasting accuracy across all models, with LSTM consistently
outperforming VAR, Random Forest, Decision Tree, and XGBoost regressors. Accuracy was
further enhanced by optimising the number of engineered features included in forecasting. The
findings highlight that effective feature engineering, combined with rigorous model validation,
can achieve high forecasting accuracy without relying on overly complex models, making this
approach valuable for practical applications such as housing market prediction and financial
time series analysis. Unlike prior studies focused mainly on model sophistication, this work
emphasises the role of feature engineering and feature selection in improving multivariate time
series forecasting and provides novel insights into balancing model complexity with feature optimisation.
