{"id":15047121,"url":"https://github.com/ahmetzamanis/weatheranomalydetectionclassification","last_synced_at":"2026-01-02T03:36:39.032Z","repository":{"id":186988763,"uuid":"659581997","full_name":"AhmetZamanis/WeatherAnomalyDetectionClassification","owner":"AhmetZamanis","description":"Time series anomaly detection, time series classification \u0026 dynamic time warping, performed on a dataset of Canadian weather measurements.","archived":false,"fork":false,"pushed_at":"2024-02-06T07:00:35.000Z","size":24910,"stargazers_count":3,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-01-20T15:30:45.076Z","etag":null,"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"],"latest_commit_sha":null,"homepage":"https://ahmetzamanis.github.io/WeatherAnomalyDetectionClassification/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/AhmetZamanis.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2023-06-28T06:25:24.000Z","updated_at":"2024-03-27T20:51:01.000Z","dependencies_parsed_at":"2024-02-05T15:30:01.442Z","dependency_job_id":"85fde4c3-96b0-44fe-bb3c-6f220a3774c1","html_url":"https://github.com/AhmetZamanis/WeatherAnomalyDetectionClassification","commit_stats":null,"previous_names":["ahmetzamanis/weatheranomalydetectionclassification"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AhmetZamanis%2FWeatherAnomalyDetectionClassification","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AhmetZamanis%2FWeatherAnomalyDetectionClassification/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AhmetZamanis%2FWeatherAnomalyDetectionClassification/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AhmetZamanis%2FWeatherAnomalyDetectionClassification/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/AhmetZamanis","download_url":"https://codeload.github.com/AhmetZamanis/WeatherAnomalyDetectionClassification/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243473527,"owners_count":20296566,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["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"],"created_at":"2024-09-24T20:54:34.905Z","updated_at":"2026-01-02T03:36:38.992Z","avatar_url":"https://github.com/AhmetZamanis.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# WeatherAnomalyDetectionClassification\nThis repository holds the scripts and reports for a project on time series anomaly detection, time series classification \u0026 dynamic time warping, performed on a dataset of Canadian weather measurements. The data was sourced from [OpenML](https://openml.org/search?type=data\u0026status=active\u0026id=43843\u0026sort=runs), shared by user Elif Ceren Gök.\n\n## Time series anomaly detection\nMultivariate time series anomaly detection using [PyOD](https://github.com/yzhao062/pyod) algorithms \u0026 the [Darts](https://github.com/unit8co/darts) package: K-means clustering, Gaussian Mixture Models, ECOD, Isolation Forest and an Autoencoder with PyTorch Lightning. Visualizing \u0026 comparing the results with multiple plots, including 3D interactive Plotly scatterplots. \n\\\n[Full report](https://ahmetzamanis.github.io/WeatherAnomalyDetectionClassification/)\n\\\n[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) \n\n## Time series classification\nMultivariate time series classification using [sktime](https://github.com/sktime/sktime) and [pyts](https://github.com/johannfaouzi/pyts): kNN with DTW distance, ROCKET \u0026 Arsenal, WEASELMUSE and a PyTorch Lightning convolutional neural network trained on image transformed data. Visualizing \u0026 comparing the performances of all algorithms.\n\\\n[Full report](https://github.com/AhmetZamanis/WeatherAnomalyDetectionClassification/blob/main/ReportClassification.md)\n\\\n[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)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fahmetzamanis%2Fweatheranomalydetectionclassification","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fahmetzamanis%2Fweatheranomalydetectionclassification","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fahmetzamanis%2Fweatheranomalydetectionclassification/lists"}