# Context Matters: Leveraging Contextual Features for Time Series Forecasting

arXiv: https://arxiv.org/abs/2410.12672 · HTML: https://mlsanity.com/paper/2410.12672/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2024.

## Built on by (most cited first, 2 of 2)

- [UniDiff: A Unified Diffusion Framework for Multimodal Time Series Forecasting](https://mlsanity.com/paper/2512.07184/) (2025)
- [Rethinking Multimodal Fusion for Time Series: Text Modalities Need Constrained Fusion](https://mlsanity.com/paper/2603.22372/) (2026)

## Abstract (arXiv)

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.