1. Time-Series Forecasting Overview Moving Averages Exponential Smoothing Seasonality

2. 2 Time Series Forecasting Time series data is simply a set of values of some variable measured at regular intervals over time. One data set (variable) over time. Based on historical data. The more data the better. Assumption: Past behavior helps us predict future behavior. Time series data can have one or more of the following components / factors / variations. Trend Seasonal Cyclical Random Moving Averages Exponential Smoothing Seasonal Methods

3. 3 Moving Averages

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7. 7 Exponential Smoothing

8. 8 Multiplicative Method

9. 9 1.For each year, calculate the average demand for each season by dividing annual demand by the number of seasons per year 2.For each year, divide the actual demand for each season by the average demand per season, resulting in a seasonal index for each season 3.Calculate the average seasonal index for each season using the results from Step 2 4.Calculate each season’s forecast for next year Multiplicative seasonal method, whereby seasonal factors are multiplied by an estimate of the average demand to arrive at a seasonal forecast

10. 10 Multiplicative Method Year1Year2Year3Year4Yr5Forecast Q14570100 132.82 Q2335370585725843.62 Q352059083011601300.03 Q4100170285215323.52 Totals10001200180022002600 Average250300450550650 SFYr1SFYr2SFYr3SFYr4AvgSF Q10.180.230.220.180.20 Q21.341.231.301.321.30 Q32.081.971.842.112.00 Q40.400.570.630.390.50

11. Homework (#7) b. Use exponential smoothing with a smoothing constant of 0.10 to forecast sales for the months May through December. Start with a January forecast of 20. c. Use exponential smoothing with a smoothing constant of 0.90 to forecast sales for the months May through December. Start with a January forecast of 20. d. Compute the errors for each forecasting period for each method. Use absolute values so that all errors are positive. Next average the errors (for the periods May through December) for each of the three forecasting methods. Which method gives the smallest mean error, i.e. is best? Applying Time Series Techniques Moving Averages Exponential smoothing alpha=0.10 Exponential smoothing alpha=0.90

12. Homework (#7) Applying Time Series Techniques Multiplicative Seasonal Method for handling data with seasonal trends.