Full set of results for LPS submitted to Data Mining and Knowledge Discovery. The excel file has 9 worksheets named as:

1. ProblemChar: Summary of the problem characteristics (75 datasets)
2. Comparison: Test error rates for LPS and competitor algorithms (Tables 1 and 2 in the manuscript)
3. ComparisonGroups: Test error rates and ranks for LPS and competitor algorithms based on the problem category
4. ComparisonGroupsReduced: Test error rates and ranks for LPS and selected competitor algorithms based on the problem category
5. LPSBest: Test error rates for each replication (10 replications, ntree=200) with selected parameters and leave-one-out (LOO) cross-validation (CV) accuracy over training datasets with selected parameters (segment length and depth settings)
6. LPSFullRegr.Splits: Test error rates for each replication (10 replications, ntree=200) for LPS with regression splits and fixed parameters (random segment length, depth=6)
7. LPSFullRandomSplits: Test error rates for each replication (10 replications, ntree=200) for LPS with radom splits and fixed parameters (random segment length, depth=6)
8. CVAccuracyOther: LOO CV accuracies of all classifiers (to draw Texas Sharpshooter Plot)
9. LinesBagnallTest: Error rates from J. Lines and A. Bagnall. Time series classification with ensembles of elastic distance measures. Data Mining and Knowledge Discovery, pages 1–28, 2014