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problemDoc.json 895 B

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  1. {
  2. "about": {
  3. "problemID": "database_problem_2",
  4. "problemName": "Database problem of type COUNTS_PER_USER",
  5. "problemSchemaVersion": "4.0.0",
  6. "problemVersion": "4.0.0",
  7. "taskKeywords": [
  8. "regression",
  9. "multivariate"
  10. ]
  11. },
  12. "inputs": {
  13. "data": [
  14. {
  15. "datasetID": "database_dataset_2",
  16. "targets": [
  17. {
  18. "targetIndex": 0,
  19. "resID": "learningData",
  20. "colIndex": 2,
  21. "colName": "posts_count"
  22. },
  23. {
  24. "targetIndex": 1,
  25. "resID": "learningData",
  26. "colIndex": 3,
  27. "colName": "comments_count"
  28. }
  29. ]
  30. }
  31. ],
  32. "performanceMetrics": [
  33. {
  34. "metric": "rootMeanSquaredError"
  35. }
  36. ]
  37. },
  38. "expectedOutputs": {
  39. "predictionsFile": "predictions.csv",
  40. "scoresFile": "scores.csv"
  41. }
  42. }

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)