https://github.com/ahmetzamanis/weatheranomalydetectionclassification

Time series anomaly detection, time series classification & dynamic time warping, performed on a dataset of Canadian weather measurements.
https://github.com/ahmetzamanis/weatheranomalydetectionclassification

autoencoder convolutional-neural-networks darts data-science deep-learning dynamic-time-warping gaussian-mixture-models isolation-forest k-means-clustering machine-learning neural-network plotly principal-component-analysis pyod python pytorch-lightning rocket sktime time-series-anomaly-detection time-series-classification

Last synced: 18 days ago
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Time series anomaly detection, time series classification & dynamic time warping, performed on a dataset of Canadian weather measurements.

• Host: GitHub
• URL: https://github.com/ahmetzamanis/weatheranomalydetectionclassification
• Owner: AhmetZamanis
• Created: 2023-06-28T06:25:24.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-02-06T07:00:35.000Z (about 1 year ago)
• Last Synced: 2024-11-19T12:55:22.428Z (3 months ago)
• Topics: autoencoder, convolutional-neural-networks, darts, data-science, deep-learning, dynamic-time-warping, gaussian-mixture-models, isolation-forest, k-means-clustering, machine-learning, neural-network, plotly, principal-component-analysis, pyod, python, pytorch-lightning, rocket, sktime, time-series-anomaly-detection, time-series-classification
• Language: Python
• Homepage: https://ahmetzamanis.github.io/WeatherAnomalyDetectionClassification/
• Size: 23.8 MB
• Stars: 3
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# WeatherAnomalyDetectionClassification

This repository holds the scripts and reports for a project on time series anomaly detection, time series classification & dynamic time warping, performed on a dataset of Canadian weather measurements. The data was sourced from [OpenML](https://openml.org/search?type=data&status=active&id=43843&sort=runs), shared by user Elif Ceren Gök.

## Time series anomaly detection

Multivariate time series anomaly detection using [PyOD](https://github.com/yzhao062/pyod) algorithms & the [Darts](https://github.com/unit8co/darts) package: K-means clustering, Gaussian Mixture Models, ECOD, Isolation Forest and an Autoencoder with PyTorch Lightning. Visualizing & comparing the results with multiple plots, including 3D interactive Plotly scatterplots. 

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[Full report](https://ahmetzamanis.github.io/WeatherAnomalyDetectionClassification/)

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[Scripts](https://github.com/AhmetZamanis/WeatherAnomalyDetectionClassification/tree/main/ScriptsAnomDetect), [Lightning classes](https://github.com/AhmetZamanis/WeatherAnomalyDetectionClassification/blob/main/X_LightningClassesAnom.py), [functions](https://github.com/AhmetZamanis/WeatherAnomalyDetectionClassification/blob/main/X_HelperFunctionsAnom.py) 

## Time series classification

Multivariate time series classification using [sktime](https://github.com/sktime/sktime) and [pyts](https://github.com/johannfaouzi/pyts): kNN with DTW distance, ROCKET & Arsenal, WEASELMUSE and a PyTorch Lightning convolutional neural network trained on image transformed data. Visualizing & comparing the performances of all algorithms.

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[Full report](https://github.com/AhmetZamanis/WeatherAnomalyDetectionClassification/blob/main/ReportClassification.md)

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[Scripts](https://github.com/AhmetZamanis/WeatherAnomalyDetectionClassification/tree/main/ScriptsClassification), [Lightning classes](https://github.com/AhmetZamanis/WeatherAnomalyDetectionClassification/blob/main/X_LightningClassesClassif.py), [functions](https://github.com/AhmetZamanis/WeatherAnomalyDetectionClassification/blob/main/X_HelperFunctionsClassif.py)