1. Time Series Analysis Whiteside

2. Pupose zTo identify the components of variation in the time series zComponents ySecular trend yCyclical variation ySeasonal variation yResidual or error variation

3. Secular trend zOver the long term, is the series changing on average ylong term is relative to the time period considered zHow? yIncrease vs. decrease yLinear? Faster? Slower? xexponential xother

4. Cyclical variation zUndulating, wave-like change around the trend zIn business data, cyclical variation is tied to cycles of the economy as a whole zEconomic and financial data is usually cyclical zCycles can be several months to several years in length zPeaks and troughs are unpredictable

5. Seasonal variation zRepeating patterns within a year zSeasonal variation is predictable zSeasonal variation is measured by seasonal index numbers zA season can be a month, a quarter, a week, etc.

6. Erratic or residual variation zRemaining variation after other components zUnpredictable, usually unexplainable

7. Identifying components - superimpose zRaw data zTrend equation zMoving average

8. Analysis zTrend - Recognized by the plot of expected model values zCycle - Recognized by the difference between the trend and the moving average zSeasonality - Recognized by the difference between the raw data and the seasonality

9. Summary zSeries may have none to all of components zPredictable series are dominated by trend and/or seasonality zUnpredictable series are dominated by cycle and/or erratic variation

10. Using NCSS decompositon zTrend - linear is the only choice, zSeasonality - seasonal index numbers are plotted and provided numerically zCycle - optional, must be input for true forecasts, plots available