**Website is still under construction and missing some important links (April 19th, 2022)**

This is a supporting page to our paper – ** Time series representation and similarity based on local autopatterns(LPS)**

by Mustafa Gokce Baydogan and George Runger

This research is supported by the Scientific and Technological Research Council of Turkey (TUBITAK) Grant Number 114C103.

**Data Mining and Knowledge Discovery**in June, 2015.

LPS is compared to nearest neighbors (NN) classifiers discussed by Lines and Bagnall (2014) on 75 datasets from different sources. See Lines and Bagnall (2014) for details of the classifiers and the datasets. Our detailed results are provided as an Excel file in the related files section. Here is the

.

**LPSBest**has best average rank and

**LPS**is second best. Based on the Friedman test, we find that there is a significant difference between the 13 classifiers at the 0.05 level. The critical differences at significance level 0.05 and 0.10 are 2.107 and 1.957, respectively.

The most important parameter in LPS is the segment length setting as discussed. We introduce two strategies for the segment length setting. The first strategy sets the segment length $L$ as the proportion of full time series length, $\gamma \in \{0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95\}$, and, furthermore, we consider the depth $D \in \{2,4,6\}$ based on a leave-one-out (LOO) cross-validation (CV) on the training data. For the cross-validation, we train 25 trees ($J=25$) for each fold. This version of LPS is named as **LPSBest **as it is analogous to **DTWBest**. Hence, we allow for $\|\gamma\| \times \|D\|=7 \times 3 = 21$ model evaluations in our study. After the parameters providing the best cross-validation accuracy are obtained, we train $J=200$ trees in the ensemble with the selected parameters to obtain the final representation. The detailed results of LOO-CV are provided in

.

**LPS.**The values of the parameters is set the same for all datasets to illustrate the robustness of LPS. In other words, no parameter tuning is conducted for this strategy.

CODES

We implemented LPS as an R package. The source files are available at CRAN.

Recently, we also made a MATLAB implementation of LPS available. The source file is provided here in the files section. This version is a slightly different version than the one implemented with R but the overall idea is the same.

HOW TO RUN LPS

REFERENCES