CRAN

DTWBI 1.1

Imputation of Time Series Based on Dynamic Time Warping

Released Jul 11, 2018 by Emilie Poisson-Caillault

Dependencies

entropy 1.2.1 dtw 1.20-1 e1071 1.7-0 rlist 0.4.6.1 lsa 0.73.1

Functions to impute large gaps within time series based on Dynamic Time Warping methods. It contains all required functions to create large missing consecutive values within time series and to fill them, according to the paper Phan et al. (2017), . Performance criteria are added to compare similarity between two signals (query and reference).

Installation

Maven

This package can be included as a dependency from a Java or Scala project by including the following your project's pom.xml file. Read more about embedding Renjin in JVM-based projects.

<dependencies>
  <dependency>
    <groupId>org.renjin.cran</groupId>
    <artifactId>DTWBI</artifactId>
    <version>1.1-b1</version>
  </dependency>
</dependencies>
<repositories>
  <repository>
    <id>bedatadriven</id>
    <name>bedatadriven public repo</name>
    <url>https://nexus.bedatadriven.com/content/groups/public/</url>
  </repository>
</repositories>

View build log

Renjin CLI

If you're using Renjin from the command line, you load this library by invoking:

library('org.renjin.cran:DTWBI')

Test Results

This package was last tested against Renjin 0.9.2657 on Aug 18, 2018.

• DTWBI_univariate-examples
• compute.fa2-examples
• compute.fb-examples
• compute.fsd-examples
• compute.nmae-examples
• compute.rmse-examples
• compute.sim-examples
• dist_afbdtw-examples
• gapCreation-examples
• local.derivative.ddtw-examples
• minCost-examples

Source

View GitHub Mirror

Release History

• 1.1
• 1.0