Time-series benchmarks based on frequency features for fair comparative evaluation.

Abstract

Time-series prediction and imputation receive lots of attention in academic and industrial areas. Machine learning methods have been developed for specific time-series scenarios; however, it is difficult to evaluate the effectiveness of a certain method on other new cases. In the perspective of frequency features, a comprehensive benchmark for time-series prediction is designed for fair evaluation. A prediction problem generation process, composed of the finite impulse response filter-based approach and problem setting module, is adopted to generate the NCAA2022 dataset, which includes 16 prediction problems. To reduce the computational burden, the filter parameters matrix is divided into sub-matrices. The discrete Fourier transform is introduced to analyze the frequency distribution of transformed results. In addition, a baseline experiment further reflects the benchmarking capability of NCAA2022 dataset.