1. Forecasting is the art and science of predicting future events

2. Forecasting Process 6. Check forecast accuracy with one or more measures 1. Identify the purpose of forecast 2. Collect historical data 3. Plot data and identify patterns 4. Select a forecast model that seems appropriate for data 5. Develop / compute forecast for period of historical data 8b. Select new forecast model or adjust parameters of existing model 8a. Forecast over planning horizon 9. Adjust forecast based on additional qualitative information and insight 10. Monitor results and measure forecast accuracy 7. Is accuracy of forecast acceptable?

3. Forecasting Methods Time Series Models (data changes with time) Causal Models (data is dependent on some other data variable) Qualitative Analysis (no relevant data is available)

4. Causal Forecasts Assumption: One or more variables can be identified which has a relationship with demand Approaches: Simple Linear Regression Multiple Linear Regression

5. “Time Series” Defn: A time-ordered sequence of observations that have been taken at regular intervals. Examples: past monthly demands, past annual demands. Assumption: Future values can be estimated from past values of the series.

6. Forecasting Process 6. Check forecast accuracy with one or more measures 1. Identify the purpose of forecast 2. Collect historical data 3. Plot data and identify patterns 4. Select a forecast model that seems appropriate for data 5. Develop / compute forecast for period of historical data 8b. Select new forecast model or adjust parameters of existing model 8a. Forecast over planning horizon 9. Adjust forecast based on additional qualitative information and insight 10. Monitor results and measure forecast accuracy 7. Is accuracy of forecast acceptable?

7. Demand Behavior Trend gradual, long-term up or down movement Cycle up & down movement repeating over long time frame Seasonal pattern periodic oscillation in demand which repeats based on calendar schedule Random movements (follow no pattern) Practice decomposing with TimeSeriesData.xls

8. Some Time Series Terms Stationary Component - a time series variable exhibiting no significant upward or downward trend over time. Nonstationary Component - a time series variable exhibiting a significant upward or downward trend over time. Seasonal Component - a time series variable exhibiting a repeating pattern at regular intervals over time. Irregular Component- something that is random and thus cannot be predicted.

9. Time Series Approaches S Moving Averages S Exponential Smoothing S Seasonal Adjustments S Linear Trend Lines

10. Moving Averages  No general method exists for determining k.  We must try out several k values to see what works best.

11. Forecast Error

12. Measuring Accuracy Magnitude Four common techniques are the: mean absolute deviation, mean absolute percent error, the mean square error, root mean square error. We will focus on MAD and MAPE.

13. A Comment on Comparing MAD and MAPE Values Care should be taken when comparing MAD and/or MAPE values of two different forecasting techniques. The lowest number may result from a technique that fits older values very well but fits recent values poorly. Plotting historical forecasts on the graph will help you identify this case. It is sometimes wise to compute the measures using only the most recent values.

14. Exponential Smoothing New forecast= Last Period Forecast + Correction for Error made Last Period = Last Period Forecast + α ( Last Period Demand – Last Period Forecast)

15. Examples of Two Exponential Smoothing Functions

16. Stationary Seasonal Effects

17. Stationary Data and Seasonal Effects Additive Effect: Multiplicative Effect: E t is the expected level at time period t. S t is the seasonal factor for time period t. p represents the number of seasonal periods