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arXiv · 2204.03456

Few-Shot Forecasting of Time-Series with Heterogeneous Channels

Lukas Brinkmeyer·Rafael Rego Drumond·Johannes Burchert·Lars Schmidt-Thieme
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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.

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Lukas Brinkmeyer, Rafael Rego Drumond, Johannes Burchert, Lars Schmidt-Thieme. 2022-04-07. Few-Shot Forecasting of Time-Series with Heterogeneous Channels. https://arxiv.org/abs/2204.03456

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