1. Time Series Anomaly Detection Experiments This file contains full color, large scale versions of the experiments shown in the paper, and additional experiments which were omitted because of space constraints Note that in every case, all the data is freely available

2. Here the bitmaps are almost the same. Here the bitmaps are very different. This is the most unusual section of the time series, and it coincidences with the PVC. Here is a Premature Ventricular Contraction (PVC) Figure 1 Expanded

3. Figure 3 Expanded The gene sequences of mitochondrial DNA of four animals, used to create their own file icons using a chaos game representation. Note that Pan troglodytes is the familiar Chimpanzee, and Loxodonta africana and Elephas maximus are the African and Indian Elephants, respectively. The file icons show that humans and chimpanzees have similar genomes, as do the African and Indian elephants.

4. Premature ventricular contraction Supraventricular escape beat Annotations by a cardiologist Figure 6 Expanded

5. A very complex and noisy ECG, but according to a cardiologist there is only one abnormal heartbeat. The algorithm easily finds it. Figure 7 Expanded

6. Below are some examples of classification, clustering with our bitmap approach. These examples did not make it into the paper because of space limitations

7. Time Series Thumbnails A snapshot of a folder containing cardiograms when its files are arranged by “Cluster” option. Five cardiograms have been grouped into two different clusters based on their similarity. Cluster 1 (eeg 1 ~ 3): BIDMC Congestive Heart Failure Database (chfdb): record chf02 Start times at 0, 82, 150, respectively Cluster 2 (eeg 6 ~ 7): BIDMC Congestive Heart Failure Database (chfdb): record chf15 Start times at 0, 82 respectively

8. Clustering with Time Series Thumbnail Approach Cluster 1 (datasets 1 ~ 5): BIDMC Congestive Heart Failure Database (chfdb): record chf02 Start times at 0, 82, 150, 200, 250, respectively Cluster 2 (datasets 6 ~ 10): BIDMC Congestive Heart Failure Database (chfdb): record chf15 Start times at 0, 82, 150, 200, 250, respectively Cluster 3 (datasets 11 ~ 15): Long Term ST Database (ltstdb): record 20021 Start times at 0, 50, 100, 150, 200, respectively Cluster 4 (datasets 16 ~ 20): MIT-BIH Noise Stress Test Database (nstdb): record 118e6 Start times at 0, 50, 100, 150, 200, respectively Data Key

9. Clustering Extended In Ge and Smyth 2000, this dataset was explored with segmental hidden Markov models. After they careful adjusted the parameters they reported 98% classification accuracy. Using time series bitmap with virtually any parameter settings, we get perfect classifications and clustering. We can get perfect classifications using one nearest neighbor classification, or we can project the data into 2 dimensional space (see next slide) and get perfect accuracy using a simple linear classifier, a decision tree or SVD. (Dataset donated by Padhraic Smyth and Seyoung Kim) 1 25 9 24 14 28 8 12 15 13 27 2 3 26 7 5 19 17 18 22 23 20 6 10 11 16 21 4 29 55 32 38 50 40 36 44 52 56 30 34 39 41 31 43 33 37 53 35 45 42 51 46 48 49 47 54 Parameters Level 1 N = 60 n = 12 1 2 28 7 19 15 3 10 12 25 4 16 9 20 26 14 27 17 24 5 8 22 29 36 6 13 21 11 18 23 34 41 30 39 31 37 35 44 53 46 52 48 50 49 56 32 40 42 45 38 43 55 33 54 47 51 Segmental Markov model [1]

10. 0.35 0.4 0.45 0.5 0.55 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 Parameters Level 1 N = 60 n = 12 Classification The MIT ECG Arrhythmia dataset projected into 2D space using only the information from a level 2-time series bitmap. The two classes are easily separated by a simple linear classifier (gray line).