Dataset

Figure 1 & Figure 2

Dataset: 97.mat

bearing_time_series.mat

Figure 3

Dataset: A 60-second snippet of an ECG.mat (data source: https://archive.physionet.org/cgi-bin/atm/ATM)

Figure 4

Dataset: 4.csv

Figure 5

Dataset: MIT-BIH Long-Term ECG Database14157m.mat

Figure 8 (right)

Dataset: lookaheads.mat

Code: test_lookaheads_of_diff_len.m

plot_lookaheads.m

Figure 9

Dataset: link (CAISO_zone_4_.csv)

Figure 10

Dataset: castironToSteel_36760.mat

Figure 11 (top)

Dataset: RandomWalk2.mat

Figure 11 (left)

Dataset: 100_highest_discord_values_for_anomaly_case.txt

100_highest_discord_values_for_NO_anomaly_case.txt

Code: plot.m

Figure 12

Dataset: ECG.mat

time_taken_BruteForce.mat

time_taken_BruteForceMASSNotPower.mat

time_taken_BruteForceMASSPowerOf2.mat

time_taken_SCRIMP.mat

time_taken_DAMPTrueOnline.mat

time_taken_DAMPEffectivelyOnline.mat

Code: baselines_code.zip

plot_baselines.m

Figure 13

Dataset: ECG.mat

Boniol, P., et. al. Unsupervised and scalable subsequence anomaly detection in large data series. The VLDB Journal. 1-23.

Figure 14

Dataset: log_demo.txt

Code: demo_DAMP.m

Section 4 (RELATED WORK)

Dataset: oneElectricityLoadDiagram.mat

Section 6.4 (CONFUSION MATRIX)

Code: test_DAMP_confusionmatrix.m (Random seeds are given in the code)

Section 6.6 (TRILLION EXPERIMENT)

Dataset: MG.txt

Code: DAMP_trillion.m (Random seeds are given in the code)

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