Exponential Smoothing of Time Series Data in R

In "Components of Time Series Data", I discussed the components of time series data. In time series analysis, we assume that the data consist of a systematic pattern (usually a set of identifiable components) and random noise (error), which often makes the pattern difficult to identify. Most time series analysis techniques involve some form of filtering out noise to make the pattern more noticeable. Though I have discussed other components of time series data, we can describe most time series patterns in terms of two basic classes of components: trend and seasonality. The former represents a general systematic linear or nonlinear component that changes over time and does not repeat, or at least does not repeat within the time range captured by our data (e.g., a plateau followed by a period of exponential growth).