tsmeasures: Computes a feature matrix of a set of time-series
In robjhyndman/anomalous: Unusual Time Series Detection

Description Usage Arguments Value Author(s) See Also Examples

Computes various different features (e.g., entropy, trend, seasonality) of a set of time-series

1 2	tsmeasures(y, normalise = TRUE, width = ifelse(frequency(y) > 1, frequency(y), 10), window = width)

`y`	A multivariate time series
`normalise`	If `TRUE`, each time series is scaled to be normally distributed with mean 0 and sd 1
`width`	A window size for variance change, level shift and lumpiness
`window`	A window size for KLscore

An object of class features with the following components:

`lumpiness`	Variance of annual variances of remainder
`entropy`	Spectral entropy from `ForeCA` package
`ACF1`	First order of autocorrelation
`lshift`	Level shift using rolling window
`vchange`	Variance change
`cpoints`	The number of crossing points
`fspots`	Flat spots using disretization
`trend`	Strength of trend
`linearity`	Strength of linearity
`curvature`	Strength of curvature
`spikiness`	Strength of spikiness
`season`	Strength of seasonality
`peak`	Strength of peaks
`trough`	Strength of trough
`KLscore`	Kullback-Leibler score
`change.idx`	Index of the maximum KL score

Rob J Hyndman, Earo Wang, Nikolay Laptev

continuous_entropy

  y <- tsmeasures(dat0, window = 48)
  # Add mean and variance to the "features" matrix
  avg <- colMeans(dat0)
  variance <- apply(dat0, 2, var)
  y <- cbind(y, avg, variance)
  class(y) <- c("features", "matrix")