Cross-Frequency Time Series Meta-Forecasting

• URL: http://arxiv.org/abs/2302.02077v1
• Date: Sat, 4 Feb 2023 03:22:16 GMT
• Title: Cross-Frequency Time Series Meta-Forecasting
• Authors: Mike Van Ness, Huibin Shen, Hao Wang, Xiaoyong Jin, Danielle C. Maddix, Karthick Gopalswamy
• Abstract summary: We introduce the Continuous Frequency Adapter (CFA), specifically designed to learn frequency-invariant representations. CFA greatly improves performance when generalizing to unseen frequencies, providing a first step towards forecasting over larger multi-frequency datasets.
• Score: 7.809667883159047
• License: http://creativecommons.org/licenses/by/4.0/
• 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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