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arXiv · 2302.02077

Cross-Frequency Time Series Meta-Forecasting

Mike Van Ness·Huibin Shen·Hao Wang·Xiaoyong Jin·Danielle C. Maddix·Karthick Gopalswamy
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Abstract

Meta-forecasting is a newly emerging field which combines meta-learning and time series forecasting. The goal of meta-forecasting is to train over a collection of source time series and generalize to new time series one-at-a-time. Previous approaches in meta-forecasting achieve competitive performance, but with the restriction of training a separate model for each sampling frequency. In this work, we investigate meta-forecasting over different sampling frequencies, and introduce a new model, the Continuous Frequency Adapter (CFA), specifically designed to learn frequency-invariant representations. We find that CFA greatly improves performance when generalizing to unseen frequencies, providing a first step towards forecasting over larger multi-frequency datasets.

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Mike Van Ness, Huibin Shen, Hao Wang, Xiaoyong Jin, Danielle C. Maddix, Karthick Gopalswamy. 2023-02-04. Cross-Frequency Time Series Meta-Forecasting. https://arxiv.org/abs/2302.02077

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