Multi-family wavelet-based feature engineering method for short-term time series forecasting.

Accurate short-term forecasting of natural phenomena-such as temperature, electricity demand, and wind-power output-is critical for reliable planning in climate science and energy management. Forecasting accuracy can be enhanced by developing advanced feature engineering techniques, particularly through the use of wavelet transformation. This paper introduces a novel method to feature construction for short-term time series forecasting of natural phenomena, which is based on the use of the Stationary Wavelet Transform (SWT) and multi-family wavelets. The developed method allows for the extraction of signal characteristics while preserving the original data dimensionality, so each time series observation is supplemented with detailed coefficients from the wavelets. This approach includes the application of several wavelet families (e.g., Daubechies, Symlets, Coiflets, Haar, and Meyer), which increases the informativeness of the time series and improves the forecasting accuracy of neural network models, in particular LSTM. Experimental evaluation on three open datasets-meteorological variables, residential electricity demand, and wind-farm output. Relative to identically configured LSTMs trained on raw observations, the wavelet-augmented models cut error consistently: MAE by 13.6%, MSE by 17.7%, RMSE by 9.5%, and SMAPE by 13.9%. These improvements confirm that multi-family SWT features offer a dataset-agnostic route to higher short-term forecasting accuracy.