{"id":20470228,"url":"https://github.com/angus924/quant","last_synced_at":"2025-04-13T10:50:52.284Z","repository":{"id":185495627,"uuid":"673099719","full_name":"angus924/quant","owner":"angus924","description":"QUANT: A Minimalist Interval Method for Time Series Classification","archived":false,"fork":false,"pushed_at":"2025-03-11T00:02:05.000Z","size":42,"stargazers_count":22,"open_issues_count":0,"forks_count":3,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-03-27T02:11:19.287Z","etag":null,"topics":["interval","scalable","time-series-classification"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/angus924.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","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}},"created_at":"2023-07-31T21:49:33.000Z","updated_at":"2025-03-11T00:02:09.000Z","dependencies_parsed_at":null,"dependency_job_id":"f3e90364-8f91-48a0-ba27-9415b57dfefd","html_url":"https://github.com/angus924/quant","commit_stats":null,"previous_names":["angus924/quant"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/angus924%2Fquant","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/angus924%2Fquant/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/angus924%2Fquant/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/angus924%2Fquant/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/angus924","download_url":"https://codeload.github.com/angus924/quant/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248702999,"owners_count":21148116,"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":["interval","scalable","time-series-classification"],"created_at":"2024-11-15T14:12:10.645Z","updated_at":"2025-04-13T10:50:52.246Z","avatar_url":"https://github.com/angus924.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# QUANT\n\n***QUANT: A Minimalist Interval Method for Time Series Classification***\n\n[Data Mining and Knowledge Discovery](https://doi.org/10.1007/s10618-024-01036-9) / [arXiv:2308.00928](https://arxiv.org/abs/2308.00928) (preprint)\n\n\n\u003e \u003cdiv align=\"justify\"\u003eWe show that it is possible to achieve the same accuracy, on average, as the most accurate existing interval methods for time series classification on a standard set of benchmark datasets using a single type of feature (quantiles), fixed intervals, and an 'off the shelf' classifier. This distillation of interval-based approaches represents a fast and accurate method for time series classification, achieving state-of-the-art accuracy on the expanded set of 142 datasets in the UCR archive with a total compute time (training and inference) of less than 15 minutes using a single CPU core.\u003c/div\u003e\n\nPlease cite as:\n\n```bibtex\n@article{dempster_etal_2024,\n  author  = {Dempster, Angus and Schmidt, Daniel F and Webb, Geoffrey I},\n  title   = {{QUANT}: A Minimalist Interval Method for Time Series Classification},\n  year    = {2024},\n  journal = {Data Mining and Knowledge Discovery},\n}\n```\n\n## Results\n\n#### UCR Archive (142 Datasets, 30 Resamples)\n\n* [Mean Accuracy + Training/Test Times](./results/accuracy_mean_ucr142.csv)\n* [Per Resample](./results/accuracy_resamples_ucr142.csv)\n\n## Requirements\n\n* Python\n* PyTorch\n* NumPy\n* scikit-learn (or similar)\n\n## Code\n\n### [`quant.py`](./code/quant.py)\n\n## Documentation\n\n[Documentation](./doc.md)\n\n## Examples\n\n```python\nfrom quant import Quant\nfrom sklearn.ensemble import ExtraTreesClassifier\n\n[...] # load data -\u003e torch.float32, [num_examples, 1, length]\n\ntransform = Quant()\n\nX_training_transform = transform.fit_transform(X_training, Y_training)\nX_test_transform = transform.transform(X_test)\n\nclassifier = \\\nExtraTreesClassifier(\n    n_estimators = 200,\n    max_features = 0.1,\n    criterion = \"entropy\",\n    n_jobs = -1\n)\nclassifier.fit(X_training_transform, Y_training)\n\npredictions = classifier.predict(X_test_transform)\n```\n\n## Acknowledgements\n\nWe thank Professor Eamonn Keogh and all the people who have contributed to the UCR time series classification archive.\n\n\u003cdiv align=\"center\"\u003e:zap:\u003c/div\u003e","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fangus924%2Fquant","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fangus924%2Fquant","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fangus924%2Fquant/lists"}