Welcome to the UCR Time Series Classification/Clustering Page

Eamonn Keogh, Xiaopeng Xi, Li Wei, and Chotirat (Ann) Ratanamahatana 

This data resource was funded by an NSF Career Award 0237918, from 2003 to 2008, and continues to be funded through NSF awards 0803410 and 0808770. Partial funding was also made available by a gift from ISCA technologies

This webpage has been created as a public service to the data mining/machine learning community, to encourage reproducible research for time series classification and clustering. 

Note that the data here is useful for testing classification / clustering, and the accuracy of indexing techniques. However the datasets are too small to make claims about the efficiency of indexing. For this, email Dr. Keogh requesting a free CD-rom of larger datasets.  If you want datasets to test anomaly detection algorithms, many such datasets are here. A comparison of the results below with classic machine learning algorithms is here, thanks to Tony Bagnall and to Weka for this.

 Classification:

How to get the datasets:

The Synthetic Control datasets are available above, and the code to reproduce the 1-NN Euclidean Distance result is available below. For the rest of the data, read on.

In order to get the password to the data, please carefully read the points below.

1)      Do not share the password or datasets with others (exception, co-authors on the current paper).

2)      If you modify the data in anyway (add noise, add warping etc), please give the modified data back to the archive before you submit your paper (that way a diligent reviewer can test your claims while the paper is under review).

3)      Where possible, we strongly advocate testing and publishing results on all datasets (to avoid cherry-picking), unless of course you are making an explicit claim for only a certain type of data (i.e. classifying short time series). In the event you dont have space in your paper, we suggest you create an extended tech report online and point to it.

4)      If you have additional datasets, we ask that you donate them to the archive in our simple format.

5)      We strongly encourage you to make only statistically significant claims about the relative performances of algorithms/distance measures. Consider the results on the Synthetic Control dataset. It would be tempting to say that unconstrained warping beats constrained warping. But unconstrained warping gets 2 wrong and constrained warping gets 5 wrong out of 300. This is not statistically significant evidence that one is better (in fact, you can show this by doing different random shuffles of the data and getting the opposite result). In contrast, either measure is better than Euclidean distance (which gets 36 wrong) using a two-tailed, paired t-test with a p-value = 0.01. We strongly advocate reading On Comparing Classifiers by Salzberg.

6)      When you write your paper, please make reproducibility your goal. In particular, explicitly state all parameters. A good guiding principle is to ask yourself Could a smart grad student get the exact same results as claimed in this paper with a days effort?. If the answer is no, we believe that something is wrong. Help the imaginary grad student by rewriting your paper.

7)  Where possible, make your code available (as we have done below).

8)  If you are advocating a new distance/similarity measure, we strongly recommend you test and report the 1-NN accuracy (as above). Note that this does not preclude the addition of other of tests, however the 1-NN test has the advantage of having no parameters and allowing comparisons between methods.

9)     Please reference the datasets in your paper as Keogh, E., Xi, X., Wei, L. & Ratanamahatana, C. A. (2006). The UCR Time Series Classification/Clustering Homepage: www.cs.ucr.edu/~eamonn/time_series_data/

After reading the above, cut out the text to either A or B below, sign it with your full name, and email it to Dr. Keogh. If you are a grad student/post-doc, you must discuss this with your adviser first and CC him/her when requesting the password. 

 A) I have read the points above, and agree to all of them. Please send me the password.

 B) I have read the points above, but I do not agree to all of them. In particular, I do NOT agree with... (please enumerate). Nevertheless, I want the data.  Please send me the password.

Clustering:

Notes:

1. Here "First paper" means first paper to use this data, but not (necessarily) using these training/test splits. In addition the data here may have been resampled, normalized or processed in other ways. 
2. This approach, where we find the best width of the Sakoe-Chiba Band by a search over the training set is a special case of Ratanamahatana-Keogh Band classification where the threshold is the length of the time series. See Ratanamahatana, C. A. and Keogh. E. (2004). Making Time-series Classification More Accurate Using Learned Constraints and see Ratanamahatana, C. A. and Keogh. E. (2004). Everything you know about Dynamic Time Warping is Wrong.
3. D.T. Pham and A.B. Chan. (1998). Control Chart Pattern Recognition using a New Type of Self Organizing Neural Network.
4. A.C. Jalba, M.H.F. Wilkinson, J.B.T.M. Roerdink, M.M. Bayer and S. Juggins. Automatic Diatom Identification using Contour Analysis by Morphological Curvature Scale Spaces.
5. D. Eads, D. Hill, S. Davis, S. Perkins, J. Ma, R. Porter, and J. Theiler. "Genetic Algorithms and Support Vector Machines for Time Series Classification." Proc. SPIE 4787. pp. 74-85. July, 2002
6. Robert T. Olszewski. Generalized Feature Extraction for Structural Pattern Recognition in Time-Series Data. PhD thesis, Carnegie Mellon University, Pittsburgh, PA, 2001.
7. Geurts, P. (2002). Contributions to decision tree induction: bias/variance tradeoff and time series classification. PhD thesis, Department of Electrical Engineering, University of Liege, Belgium.
8. Oskar J. O. Soderkvist. Computer Vision Classification of Leaves from Swedish Trees, Master thesis, Linkoping University, Sweden, 2001.
9. Ashit Gandhi. Content-Based Image Retrieval: Plant Species Identification. Master thesis, Oregon State University, September, 2002.
10. Rath, T. & Manmatha, R. (2003). Word Image matching using dynamic time warping. CVPR, Vol. II, pp.521-527.
11. Roverso, D. (2000). Multivariate temporal classification by windowed wavelet decomposition and recurrent neural networks. In 3rd ANS International Topical Meeting on Nuclear Plant Instrumentation, Control and Human-Machine Interface, 2000.
12. Xiaopeng Xi, Eamonn Keogh, Christian Shelton, Li Wei & Chotirat Ann Ratanamahatana (2006). Fast Time Series Classification Using Numerosity Reduction. ICML 2006.
13. Cherry picking, literally meaning harvesting cherries, is used metaphorically to accuse someone of pointing at individual cases which seem to confirm his or her position, while ignoring a significant portion of related cases that may contradict it.
14. Salzberg S. On comparing classifiers: Pitfalls to avoid and a recommended approach. Data Mining and Knowledge Discovery 1997; 1:317--327
15. Keogh, E. &  Kasetty, S. (2002). On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration. In the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. July 23 - 26, 2002. Edmonton, Alberta, Canada. pp 102-111.
16. D.J. Lee, R. Schoenberger, D. Shiozawa, X. Xu, and P. Zhan, Contour Matching for a Fish Recognition and Migration Monitoring System. SPIE Optics East, Two and Three-Dimensional Vision Systems for Inspection, Control, and Metrology II, vol. 5606-05, p. 37-48, Philadelphia, PA, USA, October 25-28, 2004.
17. The fish in question are mostly close to horizontal, facing left as they swam passed the camera, however a rotation invariant version of the distance measures does give better accuracy, see  LB_Keogh Supports Exact Indexing of Shapes under Rotation Invariance with Arbitrary Representations and Distance Measures. VLDB 2006.
18. Note for Car and Plane: The donor of these two datasets asked me not to make them available until his/her journal paper is accepted. As soon as I get word, I will put this data online.

People that have Downloaded the Data: