*this study is presented in INFORMS 2012@Phoenix. The presentation is available
. Note that there might be changes on the approach (as well as results) compared to our submission.”
*the paper submitted to Data Mining and Knowledge Discovery will be available in
category in Files section.”
IMPORTANT UPDATE (April 7th, 2015): Our recent submission (
) to Data Mining and Knowledge Discovery added more multivariate time series classification data from various sources. We have added the new set of datasets in MATLAB format in the files section. The details are provided in the
(the file size is around 313 MB) . Recently added time series datasets are also shown towards the end of the table below with red font color.
|# of||# of||Dataset Size|
|Network Flow||2||4||50-997||803||534||x||Sübakan et al.|
The codes are provided in the files section. Here is the
to the folder. You will find zip file containing:
- R implementation: We use the “Random Forest” package in R so you need to install R software (http://www.r-project.org/) and then install the required library using the command install.packages(“randomForest”). We also provide a parallel implementation of SMTS for multicore computers. For parallel implementation, install “doMC” package and modify the number of cores to be used accordingly. Depending on the dataset, you can observe significant gains in computation times with the parallel implementation.
- a C code and compiled libraries: The codebook generation is implemented in C and called directly from R. The code is compiled using an 64bit Ubuntu 12.04 system (*.so file is in the archive). For Windows (32bit or 64bit) or Linux system (32bit), you need to compile the C code yourself by running “R CMD SHLIB yourpath/mts_functions.c” on your command window (in Windows) or terminal (in Linux). Recently I compiled the code for Windows 64bit version and made dll file available in the folder. You need to modify the script that points to the dll file in the data preparation file as described below.
- Scripts for data preparation and parameter selection: In order to read and prepare the data and select the parameters, two R scripts are implemented. Also in data preparation code, you will find a wrapper to use the functions implemented in C. If you are on Windows operating system, R commands to include the compiled library must be changed (must switch to *.dll from *.so after compiling the library in Windows).
- Example datasets: GunPoint and Libras dataset from UCR time series database and UCI machine learning repository respectively are provided.
We also provided our cross-validation code. There are two R scripts modified to run cross-validation for evaluation purposes. An archive (zip file) is available in the same folder.
HOW TO RUN SMTS
Please include datasets and codes in the same folder. The algorithm is run in R software (use R code “smts_Main.r to run the algorithm). If you have any problems running the code, please