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Datasets:
hahuh
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PaperBenchPro

Tasks:
Time Series Forecasting
Size:
1M<n<10M
Tags:
time-series
benchmark
forecasting
anomaly-detection
License:
Dataset card Data Studio Files
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  1. .gitattributes +22 -0
  2. Time Series/anomaly_detection/MSL/.DS_Store +0 -0
  3. Time Series/anomaly_detection/MSL/MSL_test.npy +3 -0
  4. Time Series/anomaly_detection/MSL/MSL_test_label.npy +3 -0
  5. Time Series/anomaly_detection/MSL/MSL_train.npy +3 -0
  6. Time Series/anomaly_detection/PSM/test.csv +3 -0
  7. Time Series/anomaly_detection/PSM/test_label.csv +0 -0
  8. Time Series/anomaly_detection/PSM/train.csv +3 -0
  9. Time Series/anomaly_detection/SMAP/SMAP_test.npy +3 -0
  10. Time Series/anomaly_detection/SMAP/SMAP_test_label.npy +3 -0
  11. Time Series/anomaly_detection/SMAP/SMAP_train.npy +3 -0
  12. Time Series/anomaly_detection/SMD/SMD_test.npy +3 -0
  13. Time Series/anomaly_detection/SMD/SMD_test.pkl +3 -0
  14. Time Series/anomaly_detection/SMD/SMD_test_label.npy +3 -0
  15. Time Series/anomaly_detection/SMD/SMD_test_label.pkl +3 -0
  16. Time Series/anomaly_detection/SMD/SMD_train.npy +3 -0
  17. Time Series/anomaly_detection/SMD/SMD_train.pkl +3 -0
  18. Time Series/anomaly_detection/SWaT/SWaT_Dataset_Attack_v0.xlsx +3 -0
  19. Time Series/anomaly_detection/SWaT/SWaT_Dataset_Normal_v1.xlsx +3 -0
  20. Time Series/anomaly_detection/SWaT/data.py +50 -0
  21. Time Series/anomaly_detection/SWaT/swat2.csv +3 -0
  22. Time Series/anomaly_detection/SWaT/swat_raw.csv +3 -0
  23. Time Series/anomaly_detection/SWaT/swat_train.csv +3 -0
  24. Time Series/anomaly_detection/SWaT/swat_train2.csv +3 -0
  25. Time Series/classification/EthanolConcentration/EthanolConcentration_TEST.ts +0 -0
  26. Time Series/classification/EthanolConcentration/EthanolConcentration_TRAIN.ts +0 -0
  27. Time Series/classification/FaceDetection/FaceDetection_TEST.ts +3 -0
  28. Time Series/classification/FaceDetection/FaceDetection_TRAIN.ts +3 -0
  29. Time Series/classification/Handwriting/Handwriting_TEST.ts +0 -0
  30. Time Series/classification/Handwriting/Handwriting_TRAIN.ts +0 -0
  31. Time Series/classification/Heartbeat/Heartbeat_TEST.ts +3 -0
  32. Time Series/classification/Heartbeat/Heartbeat_TRAIN.ts +3 -0
  33. Time Series/classification/JapaneseVowels/JapaneseVowels_TEST.ts +0 -0
  34. Time Series/classification/JapaneseVowels/JapaneseVowels_TRAIN.ts +0 -0
  35. Time Series/classification/PEMS-SF/PEMS-SF_TEST.ts +3 -0
  36. Time Series/classification/PEMS-SF/PEMS-SF_TRAIN.ts +3 -0
  37. Time Series/classification/SelfRegulationSCP1/SelfRegulationSCP1_TEST.ts +0 -0
  38. Time Series/classification/SelfRegulationSCP1/SelfRegulationSCP1_TRAIN.ts +0 -0
  39. Time Series/classification/SelfRegulationSCP2/SelfRegulationSCP2_TEST.ts +0 -0
  40. Time Series/classification/SelfRegulationSCP2/SelfRegulationSCP2_TRAIN.ts +0 -0
  41. Time Series/classification/SpokenArabicDigits/SpokenArabicDigits_TEST.ts +0 -0
  42. Time Series/classification/SpokenArabicDigits/SpokenArabicDigits_TRAIN.ts +3 -0
  43. Time Series/classification/UWaveGestureLibrary/UWaveGestureLibrary_TEST.ts +0 -0
  44. Time Series/classification/UWaveGestureLibrary/UWaveGestureLibrary_TRAIN.ts +0 -0
  45. Time Series/imputation/ETT-small/ETTh1.csv +0 -0
  46. Time Series/imputation/ETT-small/ETTh2.csv +0 -0
  47. Time Series/imputation/ETT-small/ETTm1.csv +0 -0
  48. Time Series/imputation/ETT-small/ETTm2.csv +0 -0
  49. Time Series/imputation/electricity/.DS_Store +0 -0
  50. Time Series/imputation/electricity/electricity.csv +3 -0
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Time Series/anomaly_detection/SWaT/data.py ADDED
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1
+ import xlrd
2
+ import csv
3
+
4
+ '''def xlsx_to_csv():
5
+ workbook = xlrd.open_workbook('SWaT_Dataset_Normal_v1.xlsx')
6
+ table = workbook.sheet_by_index(0)
7
+ with open('swat_train.csv', 'w', encoding='utf-8') as f:
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+ write = csv.writer(f)
9
+ for row_num in range(table.nrows):
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+ row_value = table.row_values(row_num)
11
+ write.writerow(row_value)
12
+
13
+ if __name__ == '__main__':
14
+ xlsx_to_csv()'''
15
+ '''import pandas as pd
16
+ import csv
17
+ f = open('swat_train2.csv','w',encoding='utf-8')
18
+ csv_writer = csv.writer(f)
19
+ csv_writer.writerow(['FIT101' ,'LIT101' ,' MV101', 'P101' ,'P102', ' AIT201', 'AIT202',
20
+ 'AIT203' ,'FIT201' ,' MV201' ,' P201', ' P202', 'P203' ,' P204', 'P205', 'P206',
21
+ 'DPIT301' ,'FIT301', 'LIT301' ,'MV301' ,'MV302', ' MV303', 'MV304' ,'P301'
22
+ , 'P302', 'AIT401', 'AIT402', 'FIT401', 'LIT401', 'P401', 'P402', 'P403', 'P404',
23
+ 'UV401' ,'AIT501', 'AIT502', 'AIT503', 'AIT504', 'FIT501', 'FIT502' ,'FIT503',
24
+ 'FIT504', 'P501', 'P502', 'PIT501', 'PIT502', 'PIT503', 'FIT601', 'P601', 'P602',
25
+ 'P603', 'Normal/Attack'])
26
+
27
+ df = pd.read_csv('swat_train.csv')
28
+ df = df.values
29
+ for u in range(1,len(df)):
30
+ tem = df[u][1:]
31
+ tem2 = []
32
+ for i in range(len(tem)-1):
33
+ tem2.append(float(tem[i]))
34
+ if(tem[-1]=='Normal'):
35
+ tem2.append(0)
36
+ else:
37
+ tem2.append(1)
38
+ csv_writer.writerow(tem2)'''
39
+ import pandas as pd
40
+ df = pd.read_csv('swat_train2.csv')
41
+ df = df.values
42
+ print(df.shape)
43
+ ano = 0
44
+ for u in range(len(df)):
45
+ if(df[u][-1]==1):
46
+ ano = ano+1
47
+ print(ano)
48
+ print(ano/len(df))
49
+
50
+
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