Retrieval Augmented Time Series Forecasting
Keywords: Time series forecasting, Retrieval augmented model, Deep learning
Abstract: Time series forecasting uses historical data to predict future trends, leveraging the relationships between past observations and available features. In this paper, we propose, RAFT, a retrieval-augmented time series forecasting method to provide sufficient inductive biases and complement the model's learning capacity. When forecasting the subsequent time frames, we directly retrieve historical data candidates from the training dataset with patterns most similar to the input, and utilize the future values of these candidates alongside the inputs to obtain predictions. This simple approach augments the model's capacity by externally providing information about past patterns via retrieval modules. Our empirical evaluations on eight benchmark datasets show that RAFT consistently outperforms contemporary baselines, an average win ratio of 86% for multivariate forecasting and 80% for univariate forecasting tasks.
Supplementary Material: zip
Primary Area: learning on time series and dynamical systems
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Submission Number: 2137
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