csts: Seasonal Time Series Regression

Description Usage Arguments Examples

Description

This function performs time series regression with seasonal considerations by computing the following (as per Dr. Chatterjee's instruction in Fall 2016): (centered) moving average, seasonl ratio, raw seasonal indices, normalized seasonal indices, de-seasonalized raw data, de-seasonalized predictions, and re-seasonalized predictions. Optionally, it can carry this forecasting method out past the end of the data set.

Usage

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csts(data, start = NULL, end = NULL, frequency = 1,
  plot.initial = FALSE, df.print = FALSE, no.predict = 0, mad = TRUE,
  mad.print = FALSE)

Arguments

data

time series data.

start

the time of the first observation. Either a single number or a vector of two integers, which specify a natural time unit and a (1-based) number of samples into the time unit. See the examples for the use of the second form.

end

the time of the last observation, specified in the same way as start.

frequency

the number of observations per unit of time.

plot.initial

a boolean indicating whether you want a plot of the initial time series.

df.print

a boolean value indicating whether you want the data frame to be printed.

no.predict

the number of predictions to perform.

mad

whether to print mean absolute deviation.

Examples

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csts()

jack-thomas/uwrfRegression documentation built on May 18, 2019, 7:16 a.m.