For Timeseries Features:
Based on your current implementation, I can suggest several advanced features to enhance your time series preprocessing capabilities:
Automatic Time Series Decomposition Implement seasonal-trend decomposition (STL) to separate time series into trend, seasonal, and residual components This would allow models to learn from each component separately, improving performance on seasonal data
Dynamic Feature Generation Add configurable lag feature windows that automatically determine optimal lag values based on autocorrelation analysis Implement change point detection to identify regime shifts in time series data
Advanced Signal Processing Features Fast Fourier Transform (FFT) layers to extract frequency domain features Wavelet transforms for multi-resolution analysis of time series data Spectral analysis features to capture cyclical patterns
Improved Missing Value Handling Add specialized interpolation methods for time series (cubic spline, LOCF, etc.) Implement masking mechanism to handle irregular time series with missing timestamps
Time-Aware Attention Mechanisms Implement temporal attention layers that focus on relevant time steps Create a positional encoding layer specifically for time series to encode temporal distance
Multi-Scale Processing Implement automatic resampling at multiple time scales (hourly, daily, weekly) Create hierarchical time series preprocessors that handle different granularities
Enhanced Seasonality Handling Add calendar feature generation (holidays, day of week, etc.) Implement multiple seasonal period detection and encoding
Causal Inference Features Add Granger causality testing as a preprocessing step Implement transfer entropy calculations for multivariate time series
Temporal Feature Extraction Add automatic feature extraction using tsfresh-inspired statistical features Implement shapelets detection for pattern recognition