https://github.com/stephanielees/time-series-classification-on-sensor-data

Time series classification to identify the state of human activity.
https://github.com/stephanielees/time-series-classification-on-sensor-data

classification deeplearning lstm shapelet-transform shapelets timeseries

Last synced: 2 days ago
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Time series classification to identify the state of human activity.

• Host: GitHub
• URL: https://github.com/stephanielees/time-series-classification-on-sensor-data
• Owner: stephanielees
• Created: 2024-01-10T11:59:13.000Z (12 months ago)
• Default Branch: main
• Last Pushed: 2024-01-11T04:58:39.000Z (12 months ago)
• Last Synced: 2024-11-08T15:12:39.410Z (about 2 months ago)
• Topics: classification, deeplearning, lstm, shapelet-transform, shapelets, timeseries
• Language: Jupyter Notebook
• Homepage:
• Size: 1.7 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# time-series-classification-on-sensor-data

## Data

The data is from a Kaggle competition Tabular Playground Series April 2022. The multivariate time series consists of 13 time series, each of which has 60 data points.

## Goal

This is a binary classification problem, so we need to predict the probability of an input being in group 1. 

## Models

I tried various approaches, both in the feature extraction and in the modelling stages. 


- Feature extraction

  * ROCKET

  * Shapelets

  * None

- Modelling

  * Gradient Boosting

  * Neural Network (the basic, a fully connected layer)

  * LSTM

  * Logistic regression

Three notebooks are included in this repository. The first two notebooks have their own repo, but I also put them here to complete the collection.

1. tps_apr2022_rocket focus on ROCKET as a feature extractor. The video is [here](https://youtu.be/0c0YNWo9Xyg)

2. shapelet_tslearn focus on Shapelets to transform the dataset. The video is [here](https://youtu.be/u69v5gm_zBk). Note that I didn't set the seed for learning shapelets, so the shapelets visualizations in this notebook may look a little bit different than what you see in the video. But that doesn't affect the model performance.

3. The video for models with low AUC is [here](https://youtu.be/LCIpAKJKrQ8). In that video I also share my opinion about what we can learn from this project.