Summary of Context Matters: Leveraging Contextual Features For Time Series Forecasting, by Sameep Chattopadhyay et al.
Context Matters: Leveraging Contextual Features for Time Series Forecasting
by Sameep Chattopadhyay, Pulkit Paliwal, Sai Shankar Narasimhan, Shubhankar Agarwal, Sandeep P. Chinchali
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed ContextFormer method is a novel way to incorporate multimodal contextual information into existing pre-trained forecasting models. By surgically integrating this information, ContextFormer can enhance the performance of base forecasters by up to 30% on real-world datasets spanning energy, traffic, environmental, and financial domains. This is particularly useful for time series forecasts that are influenced by exogenous features such as public sentiments and policy decisions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ContextFormer is a new way to make forecasting models better. It takes in lots of different kinds of information, like news articles, tweets, and numbers, and uses it to improve the predictions made by other forecasting models. This is important because sometimes what’s happening outside of the data we’re looking at can affect how accurate our predictions are. ContextFormer helps fix this problem by taking all that extra information into account. |
Keywords
» Artificial intelligence » Time series
