Performs the Transformed-Stationary Extreme Values Analysis
computeAnnualMaxima
computeMonthlyMaxima
tsEvaComputeReturnLevelsGPDFromAnalysisObj
Detrend a Time Series
Fill missing values in a time series using a moving average approach.
tsEvaPlotGPDImageSc
tsEvaPlotGPDImageScFromAnalysisObj
tsEvaPlotSeriesTrendStdDevFromAnalyisObj
tsEvaPlotTransfToStat
tsEvaTransformSeriesToStationaryPeakTrend
Transform Time Series to Stationary Trend and Change Points with Confi...
Transform Time Series to Stationary Trend and Change Points
tsEvaTransformSeriesToStationaryTrendOnly_ciPercentile
tsEvaTransformSeriesToStationaryTrendOnly
tsEvaTransformSeriesToStatSeasonal_ciPercentile
tsEVstatistics
tsGetNumberPerYear
Check if all years in a time series are present
declustpeaks
empdis: Empirical Distribution Function
Empirical Distribution Function
findMax
Initialize Percentiles
Max Daily Value Function
Parse named arguments and assign values to a predefined argument struc...
Estimate Average Seasonality
Change point detection in time series
tsEvaComputeReturnLevelsGEV
tsEvaComputeReturnLevelsGEVFromAnalysisObj
tsEvaComputeReturnLevelsGPD
Calculate the running variance of a time series with NaN handling
tsEvaComputeReturnPeriodsGEV
tsEvaComputeReturnPeriodsGPD
tsEvaComputeRLsGEVGPD
tsEvaComputeTimeRP
TsEvaNs Function
Find Trend Threshold
Calculate the return period of low flow based on a threshold and windo...
Calculate the running mean of a time series with NaN handling
tsEvaNanRunningPercentiles
tsEvaNanRunningStatistics
tsEvaPlotAllRLevelsGEV
tsEvaPlotAllRLevelsGPD
tsEvaPlotGEVImageSc
tsEvaPlotGEVImageScFromAnalysisObj
tsEvaPlotReturnLevelsGEV
tsEvaPlotReturnLevelsGEVFromAnalysisObj
tsEvaPlotReturnLevelsGPD
tsEvaPlotReturnLevelsGPDFromAnalysisObj
tsEvaPlotTransfToStatFromAnalysisObj
Calculate the running mean trend of a time series
tsEvaSampleData Function
tsEvaTransformSeriesToStationaryMMXTrend
tsEvaTransformSeriesToStationaryMultiplicativeSeasonality
tsGetPOT Function
Adaptation of the 'Matlab' 'tsEVA' toolbox developed by Lorenzo Mentaschi available here: <https://github.com/menta78/tsEva>. It contains an implementation of the Transformed-Stationary (TS) methodology for non-stationary extreme value Analysis (EVA) as described in Mentaschi et al. (2016) <doi:10.5194/hess-20-3527-2016>. In synthesis this approach consists in: (i) transforming a non-stationary time series into a stationary one to which the stationary extreme value theory can be applied; and (ii) reverse-transforming the result into a non-stationary extreme value distribution. 'RtsEva' offers several options for trend estimation (mean, extremes, seasonal) and contains multiple plotting functions displaying different aspects of the non-stationarity of extremes.
Useful links