Summary of Inceptiontime Vs. Wavelet — a Comparison For Time Series Classification, by Daniel Klenkert et al.
InceptionTime vs. Wavelet – A comparison for time series classification
by Daniel Klenkert, Daniel Schaeffer, Julian Stauch
First submitted to arxiv on: 27 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 The paper compares two approaches to classify infrasound data using neural networks. The first method involves directly classifying time series data using a custom implementation of the InceptionTime network, while the second method transforms the signals into 2D images through wavelet transformation and then uses a ResNet implementation for classification. Both approaches achieve high classification accuracy, with the direct approach reaching 95.2%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers used neural networks to classify infrasound data, comparing two different methods. One way was to directly classify time series data using a special kind of network called InceptionTime. The other method turned the signals into pictures and then used another type of network called ResNet to classify them. Both ways were very good at getting things right, with one being slightly better. |
Keywords
* Artificial intelligence * Classification * Resnet * Time series
