Summary of Framework For Variable-lag Motif Following Relation Inference in Time Series Using Matrix Profile Analysis, by Naaek Chinpattanakarn and Chainarong Amornbunchornvej
Framework for Variable-lag Motif Following Relation Inference In Time Series using Matrix Profile analysis
by Naaek Chinpattanakarn, Chainarong Amornbunchornvej
First submitted to arxiv on: 5 Jan 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 This paper proposes a framework for identifying and analyzing following patterns, or “motifs,” between two time series datasets. The authors formalize the concept of following motifs and present an efficient method, called the Matrix Profile Method, for retrieving these patterns from time series data. They compare their approach to several baselines and demonstrate its effectiveness on simulation datasets, as well as real-world datasets such as sound recordings and cryptocurrency values. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps us understand how different things move together, like a group of animals or stock market trends. It uses special methods to find patterns in time series data that show who is following whom. The paper explains a new way to discover these patterns, called “following motifs,” using a method called the Matrix Profile Method. This approach works better than other methods on some datasets and can be used in many fields to understand how different things are connected. |
Keywords
* Artificial intelligence * Time series
