tsEvaTransformSeriesToStationaryMultiplicativeSeasonality {RtsEva}

tsEvaTransformSeriesToStationaryMultiplicativeSeasonality

Description

This function decomposes a time series into a season-dependent trend and a season-dependent standard deviation.It performs a transformation from non-stationary to stationary.

Usage

tsEvaTransformSeriesToStationaryMultiplicativeSeasonality(
  timeStamps,
  series,
  timeWindow,
  seasonalityVar = TRUE
)

Arguments

timeStamps

A vector of timestamps for the time series data.

series

A vector of the time series data.

timeWindow

The size of the moving window used for trend estimation.

seasonalityVar

A logical value indicating whether to consider a time varying seasonality (30 years moving average) or a static seasonal cycle in the transformation. Default is TRUE.

Value

A list containing the transformed data and various statistics and errors.

runningStatsMulteplicity

The size of the moving window used for trend estimation

stationarySeries

The transformed stationary series

trendSeries

The trend component of the transformed series

trendSeriesNonSeasonal

The trend component of the original series without seasonality

stdDevSeries

The standard deviation component of the transformed series

stdDevSeriesNonSeasonal

The standard deviation component of the original series without seasonality

trendNonSeasonalError

The error on the non-seasonal trend component

stdDevNonSeasonalError

The error on the non-seasonal standard deviation component

trendSeasonalError

The error on the seasonal trend component

stdDevSeasonalError

The error on the seasonal standard deviation component

trendError

The overall error on the trend component

stdDevError

The overall error on the standard deviation component

Regime

The estimated regime of the trend seasonality

timeStamps

The input timestamps

nonStatSeries

The original non-stationary series

statSer3Mom

The third moment of the transformed stationary series

statSer4Mom

The fourth moment of the transformed stationary series

transformation non stationary -> stationary

transformation stationary -> non stationary y(t) = stdDev(t)*ssn_stdDev(t)*x(t) + trend(t) + ssn_trend(t) trasfData.trendSeries = trend(t) + ssn_trend(t) trasfData.stdDevSeries = stdDev(t)*ssn_stdDev(t)

Examples

timeAndSeries <- ArdecheStMartin
timeStamps <- ArdecheStMartin[,1]
series <- ArdecheStMartin[,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
TrendTh <- NA
result <- tsEvaTransformSeriesToStationaryMultiplicativeSeasonality(timeStamps,
series, timeWindow,seasonalityVar=FALSE)
plot(result$trendSeries)