DeepTime: Deep Time-index Meta-learning for Non-stationary Time-series Forecasting

Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, Steven Hoi

Keywords: time-series, forecasting, deep learning, implicit neural representation, meta-learning, time-index, non-stationary

Abstract: Advances in I.T. infrastructure has led to the collection of longer sequences of time-series. Such sequences are typically non-stationary, exhibiting distribution shifts over time -- a challenging scenario for the forecasting task, due to the problems of covariate shift, and conditional distribution shift. In this paper, we show that deep time-index models possess strong synergies with a meta-learning formulation of forecasting, displaying significant advantages over existing neural forecasting methods in tackling the problems arising from non-stationarity. These advantages include having a stronger smoothness prior, avoiding the problem of covariate shift, and having better sample efficiency. To this end, we propose DeepTime, a deep time-index model trained via meta-learning. Extensive experiments on real-world datasets in the long sequence time-series forecasting setting demonstrate that our approach achieves competitive results with state-of-the-art methods, and is highly efficient. Code is attached as supplementary material, and will be publicly released.

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TL;DR: We propose a deep time-index model which leverages a meta-learning formulation to tackle non-stationary time-series forecasting.

Community Implementations: [ 5 code implementations](https://www.catalyzex.com/paper/deeptime-deep-time-index-meta-learning-for/code)