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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