1 / 14

Lecture 4: Trend and Seasonal Series

Lecture 4: Trend and Seasonal Series. Techniques for trend Techniques for seasonality Summary Readings: Page 79-92. Techniques for Trend. Linear Trend Equation. F t = Forecast for period t t = Specified number of time periods a = Value of F t at t = 0 b = Slope of the line.

maeve
Download Presentation

Lecture 4: Trend and Seasonal Series

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Lecture 4: Trend and Seasonal Series • Techniques for trend • Techniques for seasonality • Summary • Readings: Page 79-92 ISMT/Stuart Zhu

  2. Techniques for Trend • Linear Trend Equation • Ft = Forecast for period t • t = Specified number of time periods • a = Value of Ft at t = 0 • b = Slope of the line ISMT/Stuart Zhu

  3. Sales Data for Five Weeks ISMT/Stuart Zhu

  4. y ∆y ∆t t 0 ISMT/Stuart Zhu

  5. Calculate a and b (1) (2) where n = number of periods, yi = value of the time series at time ti ISMT/Stuart Zhu

  6. Linear Trend Equation Example ISMT/Stuart Zhu

  7. 5 (2499) - 15(812) 12495 - 12180 b = = = 6.3 5(55) - 225 275 - 225 Linear Trend Calculation 812 - 6.3(15) a = = 143.5 5 y = 143.5 + 6.3t ISMT/Stuart Zhu

  8. Techniques for Seasonality • Seasonal variations • Regularly repeating movements in series values that can be tied to recurring events. ISMT/Stuart Zhu

  9. Seasonal variation Linear Trend x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x 1 2 3 4 Year ISMT/Stuart Zhu

  10. Two Models • Additive model • Demand = Trend + Seasonality • Multiplicative model • Demand = Trend * Seasonality ISMT/Stuart Zhu

  11. Using Seasonal Relatives • Seasonal relative • Percentage of average or trend • Computing Seasonal relatives • Centered moving average: a moving average positioned at the center of the data ISMT/Stuart Zhu

  12. Example 7 at Page 84 Forecast-s3 • Compute estimated relatives for each day • Suppose that the trend equation is given by 120 +5t • Give Friday represents t=5, what is the forecast on Friday? ISMT/Stuart Zhu

  13. Summary • Compute the trend equation • a, b, • How to compute and use seasonal relatives ISMT/Stuart Zhu

  14. Review Problems • Problem 5 at page 113 • Problem 10 at page 115 • Problem 11 at page 115 • Problem 12 at page 115 ISMT/Stuart Zhu

More Related