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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)

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GrooveSquid.com Paper Summaries

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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 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