Summary of Time-mmd: Multi-domain Multimodal Dataset For Time Series Analysis, by Haoxin Liu et al.
Time-MMD: Multi-Domain Multimodal Dataset for Time Series Analysis
by Haoxin Liu, Shangqing Xu, Zhiyuan Zhao, Lingkai Kong, Harshavardhan Kamarthi, Aditya B. Sasanur, Megha Sharma, Jiaming Cui, Qingsong Wen, Chao Zhang, B. Aditya Prakash
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 paper introduces Time-MMD, a comprehensive multimodal time series dataset covering 9 primary data domains. The dataset aims to overcome the limitations of existing time series analysis models by incorporating textual series data and multimodal domain-specific knowledge. The authors also develop MM-TSFlib, a library for multimodal time-series forecasting evaluations based on Time-MMD. Experimental results show significant performance enhancements through multimodality, with up to 40% mean squared error reduction in domains with rich textual data. This work has the potential to revolutionize broader applications and research topics in time series analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new dataset called Time-MMD that combines different types of data together. Right now, most models only use numbers to predict future events, but this dataset includes words too! The authors also made a special library called MM-TSFlib that helps people analyze the data better. When they tested it, they found that using all the different kinds of data helped them make more accurate predictions. This could help us do things like predict weather or stock prices better. |
Keywords
» Artificial intelligence » Time series
