Related papers: Predicting the Future by Retrieving the Past

Predicting the Future by Retrieving the Past

URL: http://arxiv.org/abs/2511.05859v1
Date: Sat, 08 Nov 2025 05:24:45 GMT
Title: Predicting the Future by Retrieving the Past
Authors: Dazhao Du, Tao Han, Song Guo,
Abstract summary: We propose Predicting the Future by Retrieving the Past (PFRP), a novel approach that explicitly integrates global historical data to enhance forecasting accuracy.<n>PFRP produces more accurate and interpretable forecasts by adaptively combining global predictions with the outputs of any local prediction model.
Score: 23.181638022094038
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Deep learning models such as MLP, Transformer, and TCN have achieved remarkable success in univariate time series forecasting, typically relying on sliding window samples from historical data for training. However, while these models implicitly compress historical information into their parameters during training, they are unable to explicitly and dynamically access this global knowledge during inference, relying only on the local context within the lookback window. This results in an underutilization of rich patterns from the global history. To bridge this gap, we propose Predicting the Future by Retrieving the Past (PFRP), a novel approach that explicitly integrates global historical data to enhance forecasting accuracy. Specifically, we construct a Global Memory Bank (GMB) to effectively store and manage global historical patterns. A retrieval mechanism is then employed to extract similar patterns from the GMB, enabling the generation of global predictions. By adaptively combining these global predictions with the outputs of any local prediction model, PFRP produces more accurate and interpretable forecasts. Extensive experiments conducted on seven real-world datasets demonstrate that PFRP significantly enhances the average performance of advanced univariate forecasting models by 8.4\%. Codes can be found in https://github.com/ddz16/PFRP.

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