An Approach to Enhance Time Series Forecasting by Fast Fourier Transform

Abstract

Feature engineering is a decisive step in time series forecasting, as it directly influences the performance of predictive models. In recent years, the Fast Fourier Transform (FFT) has gained popularity as an algorithm for extracting frequency-domain features from time series data. In this paper, we investigate the potential of using FFT as feature engineering to improve the accuracy and efficiency of time-series forecasting models. We performed a comparative analysis of the performance of models trained with FFT-based features versus traditional time domain features on two datasets. Our results demonstrate that FFT-based feature engineering outperforms traditional feature engineering methods in terms of forecast accuracy and computational efficiency. Additionally, we provide insights into the interpretability of the frequency domain features and their relationship with the underlying time series patterns. Overall, our study suggests that FFT-based feature engineering is a promising approach to enhance the performance of time-series forecasting models.