deseason_ts: Deseason Seasonal Time Series

In EWSmethods: Forecasting Tipping Points at the Community Level

Deseason Seasonal Time Series

Description

Removes seasonal signals from time series using either averaging or time series decomposition methods. Three decomposition methods are available: traditional decompostion, loess decomposition and X11 decompostion.

Usage

deseason_ts(
  data,
  increment = c("month", "year", "week", "day"),
  method = c("average", "decompose", "stl"),
  order = NULL
)

Arguments

data	The dataframe to be transformed, The first column must be a vector of dates with all other columns the individual time series.
increment	The time-step increment in either month, year, week, or day. Provides the basis for deaseasoning.
method	String of either "average", "decompose", "stl" or "x11" indicating the method of deseasoning. "average" subtracts the average representative month/week/day-of-the-year from each time point whereas "decompose", "stl" and "x11" subtracts the seasonal component estimated by time series decomposition, loess decomposition and the X11 method respectively.
order	String indicating the date format of the date columns. Options are "dmy", "ymd" or "mdy".

Value

Dataframe of deseasoned time series.

Examples

#Generate five random monthly time series
#of 5 years length.

spp_data <- matrix(nrow = 5*12, ncol = 5)
spp_data <- sapply(1:dim(spp_data)[2], function(x){
spp_data[,x] <- rnorm(5*12,mean=20,sd=5)})
multi_spp_data <- cbind("time" =
 seq(as.Date('2000/01/01'), as.Date('2004/12/01'), by="month"),
   as.data.frame(spp_data))

#Deseason using time series
#decomposition.

decomp_dat <- deseason_ts(data = multi_spp_data,
increment = "month",
method = "decompose",
order = "ymd")

#Deseason using loess

decomp_dat <- deseason_ts(data = multi_spp_data,
increment = "month",
method = "stl",
order = "ymd")

EWSmethods documentation built on May 29, 2024, 5:41 a.m.