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

• URL: http://arxiv.org/abs/2502.19983v1
• Date: Thu, 27 Feb 2025 11:03:37 GMT
• Title: Efficient Time Series Forecasting via Hyper-Complex Models and Frequency Aggregation
• Authors: Eyal Yakir, Dor Tsur, Haim Permuter,
• Abstract summary: Time series forecasting is a long-standing problem in statistics and machine learning.<n>We propose the Frequency Information Aggregation (FIA)-Net, which is based on a novel complex-valued architecture.<n>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.
• Score: 1.024113475677323
• License: http://creativecommons.org/licenses/by/4.0/
• 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/.

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