Holt Winters, Non-seasonal
Holt-Winters Exponential Smoothing is an extension of simple exponential smoothing that takes into account both level and trend components in a time series, and optionally, seasonality. When seasonality is not present in the data, the method is referred to as Holt-Winters Exponential Smoothing without seasonality, or simply non-seasonal Holt-Winters.
To analyse it in BioStat Prime user must follow the steps as given.
- Steps
Load the dataset -> Click on the Forecasting tab in main menu -> Select Holt winters, non-seasonal -> Choose variables to predict -> Write Time of first observation -> Write Number of observations per unit of time -> Execute.
Arguments
- vars
select a variable to build a model for
- start
Time of first observation should be entered in the format year,month or year,quarter e.g.( if your data is organized in months the 1992,1 for Jan 1992 or if your data is organized in quarters then 1992,1 refers to the first quarter of 1992.
- frequency
Number of observations in unit time. Example: for monthly there are 12 observation in a year. For quarterly there are 4 observation in a year.
- exponential
Determines whether exponential smoothing will be done, value set to FALSE
- seasonal
a character string "Non Seasonal" for a non seasonal model.
- plotSeries
if TRUE a time series plot will also be generated.
- saveFitted
if TRUE fit values are saved.
- plotOriginalandForecast
Plot original and forecasted series
- predict
if TRUE predicted values will also be generated.
- savePredictedVals
predicted values will be saved.
- plotPredictedValues
predicted values will also be plotted.
- correlogram
if TRUE a correlogram will be generated.
- main
main title of the plot
- ylab
title for the y axis
- dataset
the name of the dataset from which the variables have been selected