1. T18-06 - 1 T18-06 Seasonal Relatives Purpose Allows the analyst to create and analyze the "Seasonal Relatives" for a time series. A graphical display of the seasonal relationship is shown. Inputs Historical Time Series Seasonality Labels Outputs Adjusted Seasonal Relatives Graph showing Adjusted Seasonal Relatives Limitations 60 Time Series Observations 12 Seasonality Labels

2. T18-06 - 2 Time Series Techniques Seasonal Relatives – develops factors based on seasonality. These factors are used to adjust future forecasts Horizon: Intermediate range Method: Strength: Ability to determine a seasonality factors to adjust future forecasts. Weakness: Lot of effort when no seasonality exists. Good idea to look at data to determine if seasonality should be considered.

3. T18-06 - 3 Given that a forecast is rarely correct, the methodology you choose should be the one which provides the least error from the actual historical demand. Forecast error is defined as the difference between actual historical demand and the forecast. Forecast Accuracy

4. T18-06 - 4 Forecast Error

5. T18-06 - 5 There are two measures used to monitor the accuracy of a forecast. The Mean Absolute Deviation (MAD) and the Mean Squared Error (MSE). The MAD is the average of the absolute value of the forecast errors. The MSE is the average of the squared forecast errors. Note : The formula for the MSE shown above may vary slightly. Some textbooks divide the sum of the squared errors by n-1 rather than n. Monitoring the Forecast

6. T18-06 - 6 A company has looked at its quarterly sales over the last three years, and believes that in addition to a linear trend a seasonal pattern is present. Determine the adjusted seasonal relatives. Trend Adjusted Exponential Smoothing Example

7. T18-06 - 7 Input the Seasonality Label and Time Series in the light green cells. The adjusted seasonal relatives are automatically calculated.

8. T18-06 - 8 A graph showing the Adjusted Seasonal Relatives is automatically produced.