Few-Shot Forecasting of Time-Series with Heterogeneous Channels
Abstract
Learning complex time series forecasting models usually requires a large amount of data, as each model is trained from scratch for each task/data set. Leveraging learning experience with similar datasets is a well-established technique for classification problems called few-shot classification. However, existing approaches cannot be applied to time-series forecasting because i) multivariate time-series datasets have different channels and ii) forecasting is principally different from classification. In this paper we formalize the problem of few-shot forecasting of time-series with heterogeneous channels for the first time. Extending recent work on heterogeneous attributes in vector data, we develop a model composed of permutation-invariant deep set-blocks which incorporate a temporal embedding. We assemble the first meta-dataset of 40 multivariate time-series datasets and show through experiments that our model provides a good generalization, outperforming baselines carried over from simpler scenarios that either fail to learn across tasks or miss temporal information.
Keywords
Cite
@article{arxiv.2204.03456,
title = {Few-Shot Forecasting of Time-Series with Heterogeneous Channels},
author = {Lukas Brinkmeyer and Rafael Rego Drumond and Johannes Burchert and Lars Schmidt-Thieme},
journal= {arXiv preprint arXiv:2204.03456},
year = {2022}
}
Comments
Under review. Equal contribution (Brinkmeyer and Rego Drumond)