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synthetic_control

synthetic_control

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Author: Dr Robert Alcock (rob@skyblue.csd.auth.gr) Source: [UCI](https://archive.ics.uci.edu/ml/datasets/Synthetic+Control+Chart+Time+Series) - 1999 Please cite: Synthetic Control Chart Time Series This data consists of synthetically generated control charts. This dataset contains 600 examples of control charts synthetically generated by the process in Alcock and Manolopoulos (1999). There are six different classes of control charts: 1. Normal 2. Cyclic 3. Increasing trend 4. Decreasing trend 5. Upward shift 6. Downward shift Past Usage Alcock R.J. and Manolopoulos Y. Time-Series Similarity Queries Employing a Feature-Based Approach. 7th Hellenic Conference on Informatics. August 27-29. Ioannina,Greece 1999. References and Further Information D.T. Pham and A.B. Chan "Control Chart Pattern Recognition using a New Type of Self Organizing Neural Network" Proc. Instn, Mech, Engrs. Vol 212, No 1, pp 115-127 1998. References 1. http://skyblue.csd.auth.gr/~rob/ 2. mailto:rob@skyblue.csd.auth.gr 3. http://kdd.ics.uci.edu/ 4. http://www.ics.uci.edu/ 5. http://www.uci.edu/

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11 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: Leave one out - target_feature: class
0 runs - estimation_procedure: 10% Holdout set - target_feature: class
0 runs - estimation_procedure: 33% Holdout set - target_feature: class
0 runs - estimation_procedure: Test on Training Data - target_feature: class
0 runs - estimation_procedure: 20% Holdout (Ordered) - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10 times 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
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