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Welcome to DTWUMI R package

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What is DTWUMI ?

  • A new method based on Dynamic Time Warping for imputation of Uncorrelated Multivariate Time series
  • A R package

Package content

  • a function to impute large gaps within multivariate time series based on Dynamic Time Warping methods. Gaps of size 1 or inferior to a defined threshold are filled using simple average and weighted moving average respectively. Larger gaps are filled using the methodology provided by Phan et al. (2017) DOI: <10.1109/MLSP.2017.8168165>: a query is built immediatly before/after a gap and a moving window is used to find the most similar sequence to this query using Dynamic Time Warping. To lower the calculation time, similar sequences are pre-selected using global features. Contrary to the univariate method (package DTWBI), these global features are not estimated over the sequence containing the gap(s), but a feature matrix is built to summarize general features of the whole multivariate signal. Once the most similar sequence to the query has been identified, the adjacent sequence to this window is used to fill the gap considered. This function can deal with multiple gaps over all the sequences componing the input multivariate signal. However, for better consistency, large gaps at the same location over all sequences should be avoided.
  • a simulated dataset from Phan et al. (2017) DOI: <10.1109/MLSP.2017.8168165>

Installation procedure

Direct procedure

Open a R session, then execute the following instructions:

devtools::install_url("", dependencies = T)

More complex procedure

Download the package: R package into your R working directory.

Open a R session, then execute the following R instructions:

install.packages(c("dtw", "rlist", "stats", "e1071", "entropy", "lsa"))
install.packages("DTWUMI\_1.0.tar.gz", repos = NULL, type = "source")

How to use it

In a R session, execute:



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