Neural Fourier Transform for Multiple Time Series Prediction

TMLR Paper6214 Authors

Abstract:

Multivariate time series forecasting is an important task in various fields such as economic planning, healthcare management, and environmental monitoring.
In this work, we present a novel methodology for improving multivariate forecasting, particularly, in data sets with strong seasonality.
We frame the forecasting task as a Multi-Dimensional Fourier Transform (MFT) problem and propose the Neural Fourier Transform (NFT) that leverages a deep learning model to predict future time series values by learning the MFT coefficients.
The efficacy of NFT is empirically validated on 7 diverse datasets, demonstrating improvements over multiple forecasting horizons and lookbacks, thereby establishing new state-of-the-art results.
Our contributions advance the field of multivariate time series forecasting by providing a model that excels in predictive accuracy.
The code of this study is publicly available.

Submission Type: Long submission (more than 12 pages of main content)

Assigned Action Editor: ~Han-Jia_Ye1

Submission Number: 6214