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datasetDoc.json 2.6 kB

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5 years ago
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  1. {
  2. "about": {
  3. "datasetID": "graph_dataset_2",
  4. "datasetName": "Test graph dataset in edgelist format",
  5. "description": "Based on LL1_EDGELIST_net_nomination_seed_dataset",
  6. "datasetSchemaVersion": "4.0.0",
  7. "redacted": false,
  8. "datasetVersion": "4.0.0",
  9. "digest": "ef138e993861a11f1d09a8b3662179eb0661c85a4e38d572be8555e32da712f1"
  10. },
  11. "dataResources": [
  12. {
  13. "resID": "learningData",
  14. "resPath": "tables/learningData.csv",
  15. "resType": "table",
  16. "resFormat": {
  17. "text/csv": [
  18. "csv"
  19. ]
  20. },
  21. "isCollection": false,
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  65. ]
  66. },
  67. {
  68. "resID": "edgeList",
  69. "resPath": "tables/edgeList.csv",
  70. "resType": "edgeList",
  71. "resFormat": {
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  118. }

全栈的自动化机器学习系统,主要针对多变量时间序列数据的异常检测。TODS提供了详尽的用于构建基于机器学习的异常检测系统的模块,它们包括:数据处理(data processing),时间序列处理( time series processing),特征分析(feature analysis),检测算法(detection algorithms),和强化模块( reinforcement module)。这些模块所提供的功能包括常见的数据预处理、时间序列数据的平滑或变换,从时域或频域中抽取特征、多种多样的检测算

Contributors (1)