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DTWBI (version 1.1)

Imputation of Time Series Based on Dynamic Time Warping

Description

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).

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Version

Install

install.packages('DTWBI')

Monthly Downloads

240

Version

1.1

License

GPL (>= 2)

Maintainer

POISSON-CAILLAULT Emilie

Last Published

July 11th, 2018

Functions in DTWBI (1.1)

compute.fa2

FA2
dataDTWBI

Six univariate signals as example for DTWBI package
dist_afbdtw

Adaptive Feature Based Dynamic Time Warping algorithm
DTWBI-package

DTWBI
DTWBI_univariate

DTWBI algorithm for univariate signals
gapCreation

Gap creation
local.derivative.ddtw

Local derivative estimate to compute DDTW
compute.nmae

Normalized Mean Absolute Error (NMAE)
compute.rmse

Root Mean Square Error (RMSE)
compute.sim

Similarity
compute.fb

Fractional Bias (FB)
minCost

DTW-based methods for univariate signals
compute.fsd

Fraction of Standard Deviation (FSD)