GunPoint: The Gun-Point Data

In LPStimeSeries: Learned Pattern Similarity and Representation for Time Series

Description

This is the Gun-Point data from The UCR Time Series Database.

Usage

data(GunPoint)

Format

GunPoint is a list with one training time series dataset and one test time series dataset provided as separate matrices. There are 50 cases (rows) for training dataset with 150 variables (columns). Similarly there are 150 cases for test dataset with 150 variables. Variables are representing the observations over time. In other words, they are ordered so that a row is a univariate time series. Originally, this is a classification problem where there are two classes. Therefore, list stores the class information for both training and test time series. This information is stored in arrays of length 50 and 150 for training and test time series respectively (so each time series is associated with a class).

Description by Chotirat Ann Ratanamahatana and Eamonn Keogh in their publication “Everything you know about Dynamic Time Warping is Wrong“ is as follows:

“...This dataset comes from the video surveillance domain. The dataset has two classes, each containing 100 instances. All instances were created using one female actor and one male actor in a single session. The two classes are: Gun-Draw: The actors have their hands by their sides. They draw a replicate gun from a hip-mounted holster, point it at a target for approximately one second, then return the gun to the holster, and their hands to their sides. Point: The actors have their gun by their sides. They point with their index fingers to a target for approximately one second, and then return their hands to their sides. For both classes, we tracked the centroid of the actor's right hands in both X- and Y-axes, which appear to be highly correlated; therefore, in this experiment, we only consider the X-axis for simplicity...“

Author(s)

Mustafa Gokce Baydogan

Source

The original data is at http://www.cs.ucr.edu/~eamonn/time_series_data/.

References

Ratanamahatana, C. A. and Keogh. E. (2004). Everything you know about Dynamic Time Warping is Wrong. In proceedings of SIAM International Conference on Data Mining (SDM05), pp.506-510 Newport Beach, CA, April 21-23

See Also

learnPattern, computeSimilarity

Examples

data(GunPoint)
set.seed(71)

## Learn patterns on GunPoint training series with default parameters
ensemble=learnPattern(GunPoint$trainseries)
print(ensemble)

## Find the similarity between test and training series based on the learned model
similarity=computeSimilarity(ensemble,GunPoint$testseries,GunPoint$trainseries)

## Find the index of 1 nearest neighbor (1NN) training series for each test series
NearestNeighbor=apply(similarity,1,which.min)

## Predicted class for each test series
predicted=GunPoint$trainclass[NearestNeighbor]
print(predicted)

Example output

LPStimeSeries 1.0-5

Call:
 learnPattern(x = GunPoint$trainseries) 
               Type of random forest: regression
                     Number of trees: 200
No. of variables tried at each split: 1

  [1] 1 2 2 1 1 2 1 2 2 1 1 1 2 1 1 1 1 2 2 2 1 2 1 1 1 2 1 1 2 1 1 2 2 1 1 1 1
 [38] 1 1 2 1 2 1 1 2 1 2 2 1 2 2 2 1 2 1 1 2 2 1 2 1 2 1 1 2 1 1 2 2 2 2 2 2 2
 [75] 2 2 2 2 2 1 1 1 2 2 2 2 1 2 1 1 1 2 1 2 1 1 2 2 1 1 2 1 1 2 2 1 1 2 1 2 2
[112] 2 1 2 1 1 2 1 1 2 1 2 2 2 1 2 1 1 2 2 2 2 1 1 1 2 1 2 1 1 2 1 1 2 1 2 1 2
[149] 2 1

LPStimeSeries documentation built on May 2, 2019, 8:25 a.m.