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Dynamic library for SMTS functions (Windows 64bit)

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You need to have this file if you want to run SMTS on Windows computers (64bit). Please change the function argument that requires the path to this library (towards the end of the data preparation code).





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2014-03-24 20:03:44
15 KB
389
Additional source codes for cross-validation (SMTS)

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SMTS code is slightly modified to enable researchers to do cross-validation. You need to download the R implementation first. See Multivariate Time Series Classification with Learned Discretization for detailed instructions.





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2013-03-09 10:17:01
2.81 KB
503
Source code of multivariate extension of TSBF

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During our revision for our SMTS paper, we have extended TSBF to multivariate time series classification (MTSBF) for comparison purposes. 

This archive (*.zip file) stores the required R files, compiled files (*.so file for linux based systems) and source codes (in C language).  If you want to run this on Windows, you need to compile c files using command "R CMD SHLIB /pathname" to generate a *.dll (dynamic library). You will need to modify the file named  'multivariateTSBF_functions.r' accordingly. This file does not check the operating system and looks for "*.so" file in its current form.

We also have a sample dataset (ECG dataset from http://www.cs.cmu.edu/~bobski/). MTSBF uses the same file structure as SMTS.

Let me know if you have any questions. 





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2013-10-28 17:52:29
87.17 KB
613
Source code of SMTS (parallel)

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R implementation for SMTS.  See Multivariate Time Series Classification with Learned Discretization for detailed instructions. Random Forests are trained in parallel in this version. This requires "doMC" package in R and it works for Linux machines. If you are on windows environment, use the other version in this folder.





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2013-03-09 10:14:52
263.88 KB
772
Source code of SMTS

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R implementation for SMTS.  See Multivariate Time Series Classification with Learned Discretization for detailed instructions. If you want to train Random Forests in parallel, find the parallel version of the code in this folder. Parallel version requires "doMC" package in R and it works for Linux machines.

 





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2013-03-10 07:56:16
217.31 KB
986
Multivariate Time Series Classification Datasets

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15 multivariate time series datasets are used to compare S-MTS with competitor algorithms. Most of the studies working on MTS classification follow a different strategy for experimentation which makes the comparison difficult. Some studies evaluates performance using cross-validation. To have fair comparison, we evaluated the performance using both a train/test split and cross-validation. 

For each dataset, there is train and test file. For cross-validation, train and test data is combined into a single data file. The details about the datasets are provided on Multivariate Time Series Classification with Learned Discretization.

For file formatting details, please click on the title above. You may want to check the source code for data preparation in the SMTS source code to transform the data in to your own format. Let me know if you have any questions.





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2012-11-01 17:14:53
51.74 MB
2017

Copyright © 2014 mustafa gokce baydogan

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