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arXiv · 2410.12672

Context Matters: Leveraging Contextual Features for Time Series Forecasting

Sameep Chattopadhyay·Pulkit Paliwal·Sai Shankar Narasimhan·Shubhankar Agarwal·Sandeep P. Chinchali
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Abstract

Time series forecasts are often influenced by exogenous contextual features in addition to their corresponding history. For example, in financial settings, it is hard to accurately predict a stock price without considering public sentiments and policy decisions in the form of news articles, tweets, etc. Though this is common knowledge, the current state-of-the-art (SOTA) forecasting models fail to incorporate such contextual information, owing to its heterogeneity and multimodal nature. To address this, we introduce ContextFormer, a novel plug-and-play method to surgically integrate multimodal contextual information into existing pre-trained forecasting models. ContextFormer effectively distills forecast-specific information from rich multimodal contexts, including categorical, continuous, time-varying, and even textual information, to significantly enhance the performance of existing base forecasters. ContextFormer outperforms SOTA forecasting models by up to 30% on a range of real-world datasets spanning energy, traffic, environmental, and financial domains.

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Sameep Chattopadhyay, Pulkit Paliwal, Sai Shankar Narasimhan, Shubhankar Agarwal, Sandeep P. Chinchali. 2025-01-13. Context Matters: Leveraging Contextual Features for Time Series Forecasting. https://arxiv.org/abs/2410.12672

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