SMTS ranked second in the gesture recognition competitionLast Updated on Tuesday, 18 October 2016 22:30 A timeseries classification challenge is organized in the context of 2nd ECML/PKDD Workshop on Advanced Analytics and Learning on Temporal Data. This is a gesture recognition task. SMTS (Symbolic Representation for Multivariate Time Series) ranked second out of 24 methods with an accuracy of 0.956 on the test data. SMTS is a very simple and efficient representation for multivariate time series. Default parameters (as discussed in the paper) simply achieved to provide a good result for this dataset.
Call for Papers INFORMS 2016 Data Mining Best Paper AwardsMonday, 16 May 2016 08:19 The Data Mining (DM) Section of INFORMS announces the SAS Data Mining Best Paper Awards to recognize excellence among its members, particularly its student members.  Two awards will be given for applied and/or methodological papers. At least one of these (possibly both) will be awarded to a student.  Two awards will be given for theoretical papers with/without a methodological component. At least one of these (possibly both) will be awarded to a student. In order to submit a paper by a student, 1. The presenting student author must be a student on or after January 1, 2016. 2. The research must have been conducted while the presenting author was a student. 3. The paper must be written by the student author(s) with minor assistance from advisors. The effort of the student(s) must comprise at least 50% of the work presented in the paper. 4. The presenting student author must be a member of the Data Mining Section. 5. The student author must be available to present the work at a session at the 2016 INFORMS Annual Meeting. 6. Papers will not be published as part of the competition. Student papers can be in any stage with regard to publication (unpublished, submitted, published, etc.).
In order to submit a paper by a nonstudent, 1. The presenting author must be a member of the Data Mining Section. 2. The presenter must be available to present the work at a session at the 2016 INFORMS Annual Meeting. 3. No version of the paper can be published or accepted at the time of submission. This is for unpublished work only.
Papers must be submitted with a maximum of 20 printed pages (1 inch margins, single column, singlespaced, 12 point font, and Times New Roman). The judging competition will consist of a DM judging panel and judges from SAS, who is generously sponsoring the competition this year. Candidates who meet the above criteria and wish to submit their paper for consideration can submit their papers to the competition chair, Dr. Mustafa Baydogan via email ( This email address is being protected from spambots. You need JavaScript enabled to view it ), by August 1^{st}, 2016. The subject of the email should be "2016 DM Best Paper Award Submission". Late submissions will not be accepted. All awardees will make presentations at the INFORMS 2016 Annual Meeting in Nashville Tennessee (November 1316, 2016). The winners will be announced at the INFORMS DM Business Meeting and all winners will receive an award certificate.
Time series classification with Fused Lasso using "lqa" packageLast Updated on Thursday, 09 April 2015 13:34 Thursday, 09 April 2015 01:50 Penalized regression approaches are pretty popular nowadays. Ridge and Lasso regression is used to learn robust regression models which handles the biasvariance tradeoff in a nice way. For time series, or in general for temporal data, fused lasso is very successful as it penalizes the L1norm of both the coefficients and their successive differences. This post will illustrate how fused lasso can be employed to learn a time series classification model. The code towards the end of this post generates a synthetic binary time series classification problem with 100 time series of length 200. One of the classes is defined by a peak between time 41 and 60 and there are 50 of them. Below is the plot of the 100 time series overlaid. Classes are colorcoded.
"lqa" package in R provides necessary tools to fit a logistic regression model with fused lasso penalties. To learn the best parameter setting, we perform a 10fold crossvalidation on this dataset. The coefficients of the best model (with the parameter providing the best crossvalidation error rate) is below:
As expected, we were able to find a good logistic regression model with fused lasso penalties. Interpretation of the regression coefficients is interesting as they determine the time series regions differentiating the classes. Below, you can find the R codes to generate the provided results. R CODE require(lqa) set.seed(455) #create 100 synthetic time series of length 200 nofseries=100 lenseries=200 series=matrix(rnorm(nofseries*lenseries),nrow=nofseries) classSeries=rep(0,nofseries) #randomly select half of them and add random values between times 4160 to create a class with peak selected=sample(nofseries,nofseries/2) series[selected,41:60]=series[selected,41:60]+runif(20,4,8) classSeries[selected]=1 #plot the series overlaid matplot(t(series),type="l",col=classSeries+1) #generating arbitrary lambda2 sequences lambda2=exp (seq (6, 1, length = 10)) print(lambda2) #check what they are #parameters to be tried is lambda1 for L1 penalty and lambda2 for L2 (fused lasso penalty) #fixing lambda1 to 1, I try to find the optimal lambda2 value from the sequence lambdas=list(1,lambda2) #run the logistic regression (binomial family for binary classification problem) cvFused=cv.lqa(classSeries,series,lambda.candidates = lambdas, intercept = FALSE, family=binomial(), penalty.family=fused.lasso,n.fold=10,loss.func = "aic.loss") #check the structure str(cvFused) #check the coefficients of the best model plot(cvFused$best.obj$coefficients)
New multivariate time series classification datasets are added to Files sectionLast Updated on Tuesday, 12 May 2015 00:33 Tuesday, 07 April 2015 02:01 During our revision for LPS, we have performed an extensive experiment on multivariate time series classification problems. This added new datasets to the existing ones that are used for our earlier study, SMTS. The new datasets are announced in LPS paper. They are now stored in "Data Sets" category in "Files" section. There are 15 multivariate time series classification datasets for researchers to perform experiments on. Earlier datasets was provided as raw text files but we decided to change the file format to "*.mat" (MATLAB). The details about the variable storing the information are provided in the download link. A screenshot of the table from the paper providing the details about the datasets is provided below:




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