FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting

• URL: http://arxiv.org/abs/2205.08897v1
• Date: Wed, 18 May 2022 12:37:54 GMT
• Title: FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting
• Authors: Tian Zhou, Ziqing Ma, Xue wang, Qingsong Wen, Liang Sun, Tao Yao, Rong Jin
• Abstract summary: We develop a textbfFrequency textbfimproved textbfLegendre textbfMemory model, or bf FiLM, to handle the dilemma between accurately preserving historical information and reducing the impact of noisy signals in the past. Our empirical studies show that the proposed FiLM improves the accuracy of state-of-the-art models by a significant margin.
• Score: 22.821606402558707
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recent studies have shown the promising performance of deep learning models (e.g., RNN and Transformer) for long-term time series forecasting. These studies mostly focus on designing deep models to effectively combine historical information for long-term forecasting. However, the question of how to effectively represent historical information for long-term forecasting has not received enough attention, limiting our capacity to exploit powerful deep learning models. The main challenge in time series representation is how to handle the dilemma between accurately preserving historical information and reducing the impact of noisy signals in the past. To this end, we design a \textbf{F}requency \textbf{i}mproved \textbf{L}egendre \textbf{M}emory model, or {\bf FiLM} for short: it introduces Legendre Polynomial projections to preserve historical information accurately and Fourier projections plus low-rank approximation to remove noisy signals. Our empirical studies show that the proposed FiLM improves the accuracy of state-of-the-art models by a significant margin (\textbf{19.2\%}, \textbf{22.6\%}) in multivariate and univariate long-term forecasting, respectively. In addition, dimensionality reduction introduced by low-rank approximation leads to a dramatic improvement in computational efficiency. We also demonstrate that the representation module developed in this work can be used as a general plug-in to improve the performance of most deep learning modules for long-term forecasting. Code will be released soon

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