Is Your Model Time-Blind? The Case for Cyclical Feature Encoding

Cyclical encoding enhances machine learning models by effectively representing periodic features such as time, enabling models to better capture cyclical patterns and relationships. This technique transforms variables like hours, days, or months into sine and cosine components, preserving their cyclical nature and improving predictive accuracy in time-dependent tasks.