AmpPred: Seasonal amplitude and predictability within time-series...

In slisovski/FourSeasons:

Description

Function that helps to define the seasonality (periodicity) of the time-series.

Usage

AmpPred(data, info.periods = 4, forecast.periods = 1, cuttoff = 70,
  amp.probs = c(0.975, 0.025), plot = TRUE)

Arguments

data	data.frame as produced by defineSeasons
info.periods	the number of periods used as prior inforamtion to generate predictions
forecast.period	the number of periods to be forecasted within each step
cuttoff	exlude incomplete periods (in the beginning and the end); the cuttoff defines a percetnage of the complete periods.
amp.probs	the qunatiles used to define the seasonal amplitude.
plot	logical, of TRUE a plot will be drawn.

Details

Based on time-series analysis, this function first decomposes the data into the following components; seasonal, trend and remainder. Next, the periodicity of the time-series will be defined by using a cosine function fitted to the seasonal component (using least-square). The minima of the cosine curve can then be used to separate the periods (nessesary in other functions of the DOSeasons package).

Value

A list with 2 data frames:

summary

data frame with amplitude and predictability for each seasonal period

output

data frame with all output variables

Author(s)

Simeon Lisovski

Examples

data(tempYNP)
sTab <- defineSeasons(tempYNP$Date, tempYNP$Tmin, frequency = "daily")
seas <- AmpPred(sTab)

slisovski/FourSeasons documentation built on July 30, 2019, 11:08 p.m.