# Transform Time Series to Stationary Trend and Change Points with Confidence Intervals This function takes a time series and transforms it into a stationary trend series with change points and confidence intervals. ```r 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. ## Returns 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 ```r 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) ```

tsEvaTransformSeriesToStationaryTrendAndChangepts_ciPercentile function