Improving Energy Price Prediction through Advanced Statistical Modeling
Energy price prediction is a crucial task, but it presents unique challenges. Many existing methods produced insufficient results, primarily due to the high level of multi-collinearity within the dataset. Additionally, metrics from shrunk regression models showed limited improvement compared to ordinary regression models. Assuring data stationarity in time-series datasets further complicated the prediction process.
To address the complexities of energy price prediction, we implemented a multifaceted solution that included:
- Development of Statistical Models: Utilizing a range of statistical models, including single and multivariable regression with OLS, robust OLS, ridge, and lasso regression, to effectively eliminate multi-collinearity within the dataset.
- (S)ARIMA(X) Models: Development of (S)ARIMA(X) models with exogenous variables to improve predictive performance compared to traditional methods.
Improved Energy Price Prediction Accuracy
Following the implementation of advanced statistical modeling techniques, the client realized the following improvements:
- Multi-Collinearity Mitigation: The developed statistical models successfully mitigated multi-collinearity issues within the dataset, improving model reliability.
- Enhanced Predictive Performance: (S)ARIMA(X) models outperformed traditional models, showing improved accuracy in energy price predictions.
- Data Stationarity: Log-transformation and differentiation techniques provided data stationarity in time-series datasets, improving the strength of the predictions.