Efficient Time Series Forecasting via Hyper-Complex Models and Frequency Aggregation

TL;DR

The paper introduces FIA-Net, a novel hyper-complex model for time series forecasting that leverages frequency domain aggregation, achieving superior accuracy and efficiency over existing methods.

Contribution

It proposes a hyper-complex MLP architecture for frequency aggregation, reducing parameters and enhancing long-term dependency handling in time series forecasting.

Findings

Outperforms state-of-the-art methods in accuracy.

Uses up to three times fewer parameters.

Demonstrates improved handling of long-term dependencies.

Abstract

Time series forecasting is a long-standing problem in statistics and machine learning. One of the key challenges is processing sequences with long-range dependencies. To that end, a recent line of work applied the short-time Fourier transform (STFT), which partitions the sequence into multiple subsequences and applies a Fourier transform to each separately. We propose the Frequency Information Aggregation (FIA)-Net, which is based on a novel complex-valued MLP architecture that aggregates adjacent window information in the frequency domain. To further increase the receptive field of the FIA-Net, we treat the set of windows as hyper-complex (HC) valued vectors and employ HC algebra to efficiently combine information from all STFT windows altogether. Using the HC-MLP backbone allows for improved handling of sequences with long-term dependence. Furthermore, due to the nature of HC operations, the HC-MLP uses up to three times fewer parameters than the equivalent standard window aggregation method. We evaluate the FIA-Net on various time-series benchmarks and show that the proposed methodologies outperform existing state of the art methods in terms of both accuracy and efficiency. Our code is publicly available on https://anonymous.4open.science/r/research-1803/.