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arXiv · 2002.02887

Meta-learning framework with applications to zero-shot time-series forecasting

Boris N. Oreshkin·Dmitri Carpov·Nicolas Chapados·Yoshua Bengio
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Abstract

Can meta-learning discover generic ways of processing time series (TS) from a diverse dataset so as to greatly improve generalization on new TS coming from different datasets? This work provides positive evidence to this using a broad meta-learning framework which we show subsumes many existing meta-learning algorithms. Our theoretical analysis suggests that residual connections act as a meta-learning adaptation mechanism, generating a subset of task-specific parameters based on a given TS input, thus gradually expanding the expressive power of the architecture on-the-fly. The same mechanism is shown via linearization analysis to have the interpretation of a sequential update of the final linear layer. Our empirical results on a wide range of data emphasize the importance of the identified meta-learning mechanisms for successful zero-shot univariate forecasting, suggesting that it is viable to train a neural network on a source TS dataset and deploy it on a different target TS dataset without retraining, resulting in performance that is at least as good as that of state-of-practice univariate forecasting models.

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BibTeXRIS

Boris N. Oreshkin, Dmitri Carpov, Nicolas Chapados, Yoshua Bengio. 2020-02-07. Meta-learning framework with applications to zero-shot time-series forecasting. https://arxiv.org/abs/2002.02887

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