tsEvaTransformSeriesToStationaryTrendAndChangepts_ciPercentile {RtsEva}

Transform Time Series to Stationary Trend and Change Points with Confidence Intervals

Description

This function takes a time series and transforms it into a stationary trend series with change points and confidence intervals.

Usage

tsEvaTransformSeriesToStationaryTrendAndChangepts_ciPercentile(
  timeStamps,
  series,
  timeWindow,
  percentile
)

Arguments

timeStamps

A vector of time stamps corresponding to the observations in the series.

series

The time series data.

timeWindow

The size of the sliding window used for detrending the series.

percentile

The percentile value used for computing the running percentile of the stationary series.

Value

A list containing the following elements:

runningStatsMulteplicity

The running statistics multiplicity

stationarySeries

The transformed stationary series

trendSeries

The trend series

trendonlySeries

The trend series without the stationary component

ChpointsSeries2

The trend series with change points

changePoints

The detected change points

trendSeriesNonSeasonal

The trend series without the seasonal component

trendError

The error on the trend

stdDevSeries

The standard deviation series

stdDevSeriesNonStep

The standard deviation series without the step change component

stdDevError

The error on the standard deviation

timeStamps

The time stamps

nonStatSeries

The original non-stationary series

statSer3Mom

The running mean of the third moment of the stationary series

statSer4Mom

The running mean of the fourth moment of the stationary series

Examples

timeAndSeries <- ArdecheStMartin

#go from six-hourly values to daily max
timeAndSeries <- max_daily_value(timeAndSeries)
timeStamps <- timeAndSeries[,1]
series <- timeAndSeries[,2]

#select only the 5 latest years
yrs <- as.integer(format(timeStamps, "%Y"))
tokeep <- which(yrs>=2015)
timeStamps <- timeStamps[tokeep]
series <- series[tokeep]
timeWindow <- 365 # 1 year
percentile <- 90
result <- tsEvaTransformSeriesToStationaryTrendAndChangepts_ciPercentile(timeStamps,
series, timeWindow, percentile)
plot(result$trendSeries)