Summary of Rating Multi-modal Time-series Forecasting Models (mm-tsfm) For Robustness Through a Causal Lens, by Kausik Lakkaraju et al.
Rating Multi-Modal Time-Series Forecasting Models (MM-TSFM) for Robustness Through a Causal Lens
by Kausik Lakkaraju, Rachneet Kaur, Zhen Zeng, Parisa Zehtabi, Sunandita Patra, Biplav Srivastava, Marco Valtorta
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Methodology (stat.ME); Machine Learning (stat.ML)
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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 Multi-modal time-series forecasting models are notoriously fragile, with minor changes in input data causing significant swings in output. This fragility is particularly concerning when these models are deployed in critical areas like finance, where incorrect predictions can have severe consequences. Researchers have recently shown that graphical transformations and advanced visual models can improve performance beyond numeric data alone. In this paper, the authors introduce a novel rating methodology to assess the robustness of Multi-Modal Time-Series Forecasting Models (MM-TSFM) through causal analysis. The method helps quantify the isolated impact of various attributes on forecasting accuracy. The authors apply their rating method to a range of numeric and multi-modal forecasting models in a large experimental setup, drawing insights on robust forecasting models and their strengths. Key findings include that multi-modal forecasting (numeric + visual), which has been shown to be more accurate than numeric forecasting in previous studies, can also be more robust in diverse settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Multi-modal time-series forecasting is important because it can help make better predictions about things like stock prices or weather patterns. Right now, these models are very fragile and can easily make mistakes if the data they use is not quite right. Researchers have been trying to figure out how to make these models more robust, so they don’t make as many mistakes. One way to do this is by using both numbers and pictures (like charts or graphs) to help the model make predictions. In this paper, the authors introduce a new way to measure how well these models are doing at making accurate predictions, and they use it to test a bunch of different forecasting models. |
Keywords
* Artificial intelligence * Multi modal * Time series
