Basic Stochastic Models
So far, we have considered two approaches for modelling time series. The first is based on an assumption that there is a fixed seasonal pattern about a trend.We can estimate the trend by local averaging of the deseasonalised data, and this is implemented by the R function decompose. The second approach allows the seasonal variation and trend, described in terms of a level and slope, to change over time and estimates these features by exponentially weighted averages. We used the HoltWinters function to demonstrate this method.
KeywordsRandom Walk White Noise Time Series Model Shift Operator Random Walk Model
Unable to display preview. Download preview PDF.