Summary of Multi-modal Forecaster: Jointly Predicting Time Series and Textual Data, by Kai Kim et al.
Multi-Modal Forecaster: Jointly Predicting Time Series and Textual Data
by Kai Kim, Howard Tsai, Rajat Sen, Abhimanyu Das, Zihao Zhou, Abhishek Tanpure, Mathew Luo, Rose Yu
First submitted to arxiv on: 11 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 This paper presents a novel approach to multimodal forecasting, leveraging the intersection of time series and textual data. The authors develop the TimeText Corpus (TTC), a carefully curated dataset comprising sequences of numbers and text aligned to timestamps, covering two domains: climate science and healthcare. This dataset addresses the lack of well-curated multimodal benchmarks in the field. Additionally, the authors propose the Hybrid Multi-Modal Forecaster (Hybrid-MMF), a multimodal language model that jointly forecasts both text and time series data using shared embeddings. While their proposed model does not outperform existing baselines, this negative result highlights the challenges inherent in multimodal forecasting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a special kind of dataset that combines numbers and words to help predict what will happen in the future. They make sure this dataset is very good quality and includes information from two different areas: studying the climate and healthcare. The researchers also design a new way to use computers to forecast both types of data at the same time. Unfortunately, their approach didn’t work as well as other methods they tested. This shows that predicting the future with words and numbers is actually very hard. |
Keywords
» Artificial intelligence » Language model » Multi modal » Time series
