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add NAB dataset Former-commit-id: 144fc3f7890ea2ea397a38cce83950243f07c1ff [formerly 51ee914920aba12988acc8b78f000c3f3a48b26b] [formerly ef80542af045d55499bc711b20ec5f7cb95d8ddc [formerly 3e0aa0de57dd3c39fc106d452608080bc97ebdb1]] [formerly 902f3a7279a6a79989dc3e785e54595e8638f9ba [formerly c84725dc68d2a017e097ae72c170fe79e30d72c9] [formerly f2e4714c60b1905c0dbb47814bb94a33d99a1dc0 [formerly 507d6b4abbea1caae7f799c161f697b37587570e]]] [formerly f8d6b8d7c78297ebabf7d64ddf9b9a316fabb90d [formerly be310386699dc1773007ce29e9d1e7baba144de1] [formerly 1aca4b0620dedb6e6b6be0467e409eec847dbb84 [formerly e395eab9f6f516b2dc16a76040d848bcfa885c72]] [formerly cb9d203e2d04a54fdb40ca83d5cd4b6fbc48dbb1 [formerly 938f3b5551f9d346e245af22cdeda0e8a4c6cdd4] [formerly 2fe834d1b84fd6709b9f0e56d436bab82985402f [formerly 0ceefa541a0f3a85b674d90e30102e526e5b33fc]]]] [formerly 740419315b74b3ade3924d98706e8d98f808589d [formerly 18e5437ef211c5b20cae9ca46f2a746f77934183] [formerly 26d627a136a475a9f35b59cf024e5834a479a2e4 [formerly c4e982cf5098acf33ab8ade73fa6ed0f40fddb82]] [formerly 9c49820d64f8967e8e64f0153890020e244b47d1 [formerly 03fa79cd28ff4341d52608f7e5a6350e14067a9f] [formerly 7bf4a741cf9812046f9f2aa739ead93f44fca750 [formerly 9999d83ba0d8576deb7e1ce1eaec5b4e917116fa]]] [formerly 83a0decfaee3bc410cfb4b5318999f13ce4bf801 [formerly e7b1bb09d4208e9e833e6b8b414c9f909f0d6138] [formerly 028619d32f9cd2bf67215a295cad7151ffba42dd [formerly 69cd27d9e940d90147faded20933092cf5a3ca0a]] [formerly 6bbd1343387e4c16e6ebdc1ea65edf3a19d45478 [formerly da00923e8e225d91f6dc5af23edf7bcd456ab1cc] [formerly 7c4f8e6a2bb14a5598d3c5ddd4c9165d0912dd5e [formerly 0fecb333a60713d8a410c6d93d703575b8f9e03f]]]]] Former-commit-id: a85941b3b90aeed83314836a46c693160af0621a [formerly 727065a4400fb5eb2fef2d52922dfabc46e4e431] [formerly a61c183d8a9d9462f7d6aca18c4cd18384b598db [formerly 4f17c638c3a77c7359972de19fe1727a3b2fda0d]] [formerly e32dde39608d23142bc771f7292380c9e39118eb [formerly 632c8bbee0d061262e619829c344c1b21278615a] [formerly 09de65e17ce3d4e89e47fbcaf7573346de569c74 [formerly 42cfe67a0ded2d750cf8a38c7257393283467404]]] [formerly 4e7a053e6912550310ef513690e026420bb069eb [formerly 73e57ce3b4821a96db9d618b31ec8f00e5426160] [formerly 5e709a32502eac1d53bdbf23823f77898dce53dc [formerly 38e1eb285493a0c57a9e6b3a06569ab779745807]] [formerly 2a082a48c97187b333aada107c56684a2dcb9fc5 [formerly 6adba8cc00103b409860388506745ffdfc0bdcaf] [formerly 7c4f8e6a2bb14a5598d3c5ddd4c9165d0912dd5e]]] Former-commit-id: 88c2e0138b08fda56cb94126a151ff32f6dbd868 [formerly 89b05b68de06d9d3afae5e4669ea9bdce5757f9e] [formerly f063ba3de662ee81fe0c7b21f4237d7d2c4bcf3b [formerly ccba2c2a90969cd669d1e5b2f86c855d307ad49b]] [formerly 4267e27b5c1c42c4c5411704e1b38df7ce7a7606 [formerly a823fbd4851ebd8558092cf8e11c22d541d196b4] [formerly 08148f74caac2a9abc98f299764e3345a304e059 [formerly 90494e66f988ac1a57be95f68a426e8f17fd010f]]] Former-commit-id: e549c884641b226188eba6f934c3ad9a7efee3cf [formerly 88a7e05850c0d2936dc78746cad6c2819adf8ec5] [formerly de4e53722a9717e92c2188f4a54665fd8eb62c4d [formerly f7fdd01bc0b32ebd755074b4f8cbbc0d7a5c568f]] Former-commit-id: 2a2faafdba702faf040f43a0b2fadcf259c1a63c [formerly e21cfea3eb2877812c007f4bcd984b5f0ee61a97] Former-commit-id: e29f2f324b8303055d2fe9257bf8a39557c82784
5 years ago
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  1. {
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  3. "artificialNoAnomaly/art_daily_perfect_square_wave.csv": [],
  4. "artificialNoAnomaly/art_daily_small_noise.csv": [],
  5. "artificialNoAnomaly/art_flatline.csv": [],
  6. "artificialNoAnomaly/art_noisy.csv": [],
  7. "artificialWithAnomaly/art_daily_flatmiddle.csv": [
  8. "2014-04-11 00:00:00"
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  10. "artificialWithAnomaly/art_daily_jumpsdown.csv": [
  11. "2014-04-11 09:00:00"
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  14. "2014-04-11 09:00:00"
  15. ],
  16. "artificialWithAnomaly/art_daily_nojump.csv": [
  17. "2014-04-11 09:00:00"
  18. ],
  19. "artificialWithAnomaly/art_increase_spike_density.csv": [
  20. "2014-04-07 23:10:00"
  21. ],
  22. "artificialWithAnomaly/art_load_balancer_spikes.csv": [
  23. "2014-04-11 04:35:00"
  24. ],
  25. "realAWSCloudwatch/ec2_cpu_utilization_24ae8d.csv": [
  26. "2014-02-26 22:05:00",
  27. "2014-02-27 17:15:00"
  28. ],
  29. "realAWSCloudwatch/ec2_cpu_utilization_53ea38.csv": [
  30. "2014-02-19 19:10:00",
  31. "2014-02-23 20:05:00"
  32. ],
  33. "realAWSCloudwatch/ec2_cpu_utilization_5f5533.csv": [
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  35. "2014-02-24 18:37:00"
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  37. "realAWSCloudwatch/ec2_cpu_utilization_77c1ca.csv": [
  38. "2014-04-09 10:15:00"
  39. ],
  40. "realAWSCloudwatch/ec2_cpu_utilization_825cc2.csv": [
  41. "2014-04-15 15:44:00",
  42. "2014-04-16 03:34:00"
  43. ],
  44. "realAWSCloudwatch/ec2_cpu_utilization_ac20cd.csv": [
  45. "2014-04-15 00:49:00"
  46. ],
  47. "realAWSCloudwatch/ec2_cpu_utilization_c6585a.csv": [],
  48. "realAWSCloudwatch/ec2_cpu_utilization_fe7f93.csv": [
  49. "2014-02-17 06:12:00",
  50. "2014-02-22 00:02:00",
  51. "2014-02-23 15:17:00"
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  53. "realAWSCloudwatch/ec2_disk_write_bytes_1ef3de.csv": [
  54. "2014-03-10 21:09:00"
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  56. "realAWSCloudwatch/ec2_disk_write_bytes_c0d644.csv": [
  57. "2014-04-09 01:30:00",
  58. "2014-04-10 14:35:00",
  59. "2014-04-13 03:00:00"
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  61. "realAWSCloudwatch/ec2_network_in_257a54.csv": [
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  64. "realAWSCloudwatch/ec2_network_in_5abac7.csv": [
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  68. "realAWSCloudwatch/elb_request_count_8c0756.csv": [
  69. "2014-04-12 17:24:00",
  70. "2014-04-22 19:34:00"
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  72. "realAWSCloudwatch/grok_asg_anomaly.csv": [
  73. "2014-01-20 08:30:00",
  74. "2014-01-21 10:45:00",
  75. "2014-01-29 00:45:00"
  76. ],
  77. "realAWSCloudwatch/iio_us-east-1_i-a2eb1cd9_NetworkIn.csv": [
  78. "2013-10-10 09:35:00",
  79. "2013-10-10 20:40:00"
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  81. "realAWSCloudwatch/rds_cpu_utilization_cc0c53.csv": [
  82. "2014-02-25 07:15:00",
  83. "2014-02-27 00:50:00"
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  85. "realAWSCloudwatch/rds_cpu_utilization_e47b3b.csv": [
  86. "2014-04-13 06:52:00",
  87. "2014-04-18 23:27:00"
  88. ],
  89. "realAdExchange/exchange-2_cpc_results.csv": [
  90. "2011-07-14 13:00:01"
  91. ],
  92. "realAdExchange/exchange-2_cpm_results.csv": [
  93. "2011-07-26 06:00:01",
  94. "2011-08-10 17:00:01"
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  96. "realAdExchange/exchange-3_cpc_results.csv": [
  97. "2011-07-14 10:15:01",
  98. "2011-07-20 10:15:01",
  99. "2011-08-13 10:15:01"
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  101. "realAdExchange/exchange-3_cpm_results.csv": [
  102. "2011-08-19 18:15:01"
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  104. "realAdExchange/exchange-4_cpc_results.csv": [
  105. "2011-07-16 09:15:01",
  106. "2011-08-02 12:15:01",
  107. "2011-08-23 08:15:01"
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  109. "realAdExchange/exchange-4_cpm_results.csv": [
  110. "2011-07-16 09:15:01",
  111. "2011-08-01 07:15:01",
  112. "2011-08-23 08:15:01",
  113. "2011-08-28 13:15:01"
  114. ],
  115. "realKnownCause/ambient_temperature_system_failure.csv": [
  116. "2013-12-22 20:00:00",
  117. "2014-04-13 09:00:00"
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  119. "realKnownCause/cpu_utilization_asg_misconfiguration.csv": [
  120. "2014-07-12 02:04:00",
  121. "2014-07-14 21:44:00"
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  123. "realKnownCause/ec2_request_latency_system_failure.csv": [
  124. "2014-03-14 09:06:00",
  125. "2014-03-18 22:41:00",
  126. "2014-03-21 03:01:00"
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  128. "realKnownCause/machine_temperature_system_failure.csv": [
  129. "2013-12-11 06:00:00",
  130. "2013-12-16 17:25:00",
  131. "2014-01-28 13:55:00",
  132. "2014-02-08 14:30:00"
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  134. "realKnownCause/nyc_taxi.csv": [
  135. "2014-11-01 19:00:00",
  136. "2014-11-27 15:30:00",
  137. "2014-12-25 15:00:00",
  138. "2015-01-01 01:00:00",
  139. "2015-01-27 00:00:00"
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  141. "realKnownCause/rogue_agent_key_hold.csv": [
  142. "2014-07-15 08:30:00",
  143. "2014-07-17 09:50:00"
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  145. "realKnownCause/rogue_agent_key_updown.csv": [
  146. "2014-07-15 04:00:00",
  147. "2014-07-17 08:50:00"
  148. ],
  149. "realTraffic/TravelTime_387.csv": [
  150. "2015-07-30 12:29:00",
  151. "2015-08-18 16:26:00",
  152. "2015-09-01 05:34:00"
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  154. "realTraffic/TravelTime_451.csv": [
  155. "2015-08-11 12:07:00"
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  157. "realTraffic/occupancy_6005.csv": [
  158. "2015-09-15 06:55:00"
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  160. "realTraffic/occupancy_t4013.csv": [
  161. "2015-09-16 08:09:00",
  162. "2015-09-17 07:55:00"
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  164. "realTraffic/speed_6005.csv": [
  165. "2015-09-17 07:00:00"
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  167. "realTraffic/speed_7578.csv": [
  168. "2015-09-11 16:44:00",
  169. "2015-09-15 14:34:00",
  170. "2015-09-16 14:14:00",
  171. "2015-09-16 17:10:00"
  172. ],
  173. "realTraffic/speed_t4013.csv": [
  174. "2015-09-16 08:04:00",
  175. "2015-09-17 08:15:00"
  176. ],
  177. "realTweets/Twitter_volume_AAPL.csv": [
  178. "2015-03-03 21:07:53",
  179. "2015-03-09 17:32:53",
  180. "2015-03-16 02:57:53",
  181. "2015-03-31 03:27:53"
  182. ],
  183. "realTweets/Twitter_volume_AMZN.csv": [
  184. "2015-03-05 19:47:53",
  185. "2015-03-11 20:57:53",
  186. "2015-04-01 21:57:53",
  187. "2015-04-08 04:52:53"
  188. ],
  189. "realTweets/Twitter_volume_CRM.csv": [
  190. "2015-03-09 19:07:53",
  191. "2015-03-19 23:07:53",
  192. "2015-03-26 19:07:53"
  193. ],
  194. "realTweets/Twitter_volume_CVS.csv": [
  195. "2015-03-04 16:02:53",
  196. "2015-03-05 19:57:53",
  197. "2015-03-26 14:07:53",
  198. "2015-04-14 22:37:53"
  199. ],
  200. "realTweets/Twitter_volume_FB.csv": [
  201. "2015-03-16 07:07:53",
  202. "2015-04-03 17:47:53"
  203. ],
  204. "realTweets/Twitter_volume_GOOG.csv": [
  205. "2015-03-13 20:22:53",
  206. "2015-03-14 16:27:53",
  207. "2015-03-22 22:52:53",
  208. "2015-04-01 05:27:53"
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  210. "realTweets/Twitter_volume_IBM.csv": [
  211. "2015-03-23 22:27:53",
  212. "2015-04-20 20:07:53"
  213. ],
  214. "realTweets/Twitter_volume_KO.csv": [
  215. "2015-03-20 13:12:53",
  216. "2015-04-08 23:42:53",
  217. "2015-04-14 14:52:53"
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  219. "realTweets/Twitter_volume_PFE.csv": [
  220. "2015-03-02 21:22:53",
  221. "2015-03-04 10:32:53",
  222. "2015-03-13 19:57:53",
  223. "2015-04-07 23:42:53"
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  225. "realTweets/Twitter_volume_UPS.csv": [
  226. "2015-03-03 00:27:53",
  227. "2015-03-04 11:07:53",
  228. "2015-03-05 15:22:53",
  229. "2015-03-24 18:17:53",
  230. "2015-03-29 16:27:53"
  231. ]
  232. }

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