Deseason Seasonal Time Series

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")
#> data successfully aggregated into monthly time steps

#Deseason using loess

decomp_dat <- deseason_ts(data = multi_spp_data,
increment = "month",
method = "stl",
order = "ymd")
#> data successfully aggregated into monthly time steps