Optimizing power and performance in time series forecasting: A feature selection approach for green computing

Key Points

• Optimizing power and performance enhances the efficiency of time series forecasting processes, leading to more sustainable computing practices.
• Key findings suggest that implementing feature selection can significantly reduce resource consumption during data processing, leading to a more effective system.
• Assessment using performance metrics reveals improvements in forecasting accuracy and efficiency when adopting this approach to feature selection.
• The analysis highlights the importance of integrating green computing principles in analytics workflows, potentially influencing future developments in sustainable technology.