089. Smoothing Techniques

A primary use of time-series analysis is to forecast future values. The general behavior of the variable can often be best discussed by examining its long term trend. However, if the time series contains too many random fluctuations or short-term seasonal changes, the trend may be somewhat obscured and difficult to observe. It is often possible to eliminate many of these confounding factors by averaging the data over several time periods. This is accomplished by the use of certain smoothing techniques that remove random fluctuations in the series, thereby providing a less obstructed view of the true behavior of the series. Two common methods of smoothing time series data are examined: a moving average and exponential smoothing.

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