上海交通大学：《生产计划与控制 Production Planning and Control》课程教学资源（课件讲稿）chap02 Forecasting 8. Methods for Seasonal Series

© Production Planning and Control Dr.GENG Na Department of Industrial Engineering Logistics Management Shanghai Jiao Tong University

Production Planning and Control Dr. GENG Na Department of Industrial Engineering & Logistics Management Shanghai Jiao Tong University

© Chapter 2 Forecasting Contents 1.Introduction 2.The Time Horizon in Forecasting 3.Classification of Forecasts 4.Evaluating Forecast 5.Notation Conventions 6.Methods for Forecasting Stationary Series 7.Trend-Based Methods 8.Methods for Seasonal Series

Chapter 2 Forecasting Contents 1. Introduction 2. The Time Horizon in Forecasting 3. Classification of Forecasts 4. Evaluating Forecast 5. Notation Conventions 6. Methods for Forecasting Stationary Series 7. Trend-Based Methods 8. Methods for Seasonal Series

图 2.8.Methods of Forecasting Trend Seasonal Series more complex time series:Trend Seasonality 0 35 30 20 15 10 5 0 Time Seasonal Decomposition Using Moving Averages Winter's Method(triple exponential smoothing)

2.8. Methods of Forecasting Trend Seasonal Series Seasonal Decomposition Using Moving Averages A more complex time series: Trend + Seasonality Winter’s Method (triple exponential smoothing)

2.8.Methods of Forecasting Trend Seasonal Series A more complex time series:Trend Seasonality Seasonal Decomposition Using Moving Averages 40 .Deseaonalized-Get 30 seasonality away; 250 .Make forecast on deseaonalized data; 10 .Get seasonality 5 back 0 Time

2.8. Methods of Forecasting Trend Seasonal Series •Deseaonalized- Get seasonality away; •Make forecast on deseaonalized data; •Get seasonality back Seasonal Decomposition Using Moving Averages A more complex time series: Trend + Seasonality

2.8.Seasonal Decomposition Using Moving Averages Example 2.7 Suppose that original demand history of a certain item for the past eight quarters is given by 10,20,26,17,12,23,30,22.The graph of this demand is shown in the following figure. 40 35 20 Time

2.8. Seasonal Decomposition Using Moving Averages Example 2.7 Suppose that original demand history of a certain item for the past eight quarters is given by 10, 20, 26, 17, 12, 23, 30, 22. The graph of this demand is shown in the following figure

2.8.Methods of Forecasting Trend Seasonal Series Procedures: Draw the demand curves and estimate the season length N; Computer the moving average MA(N): Centralize the moving averages; Get the centralized MA values back on period; Calculate seasonal factors,and make sure of >c,=N Divide each observation by the appropriate seasonal factor to obtain the deseasonalized demand .Forecast is made based on deseasonalized demand. Final forecast is obtained by multiplying the forecast(with no seasonality)with seasonal factors

2.8. Methods of Forecasting Trend Seasonal Series Procedures: • Draw the demand curves and estimate the season length N; • Computer the moving average MA(N); • Centralize the moving averages; • Get the centralized MA values back on period; • Calculate seasonal factors, and make sure of  ct=N. • Divide each observation by the appropriate seasonal factor to obtain the deseasonalized demand • Forecast is made based on deseasonalized demand. • Final forecast is obtained by multiplying the forecast (with no seasonality) with seasonal factors

2.8.Methods of Forecasting Trend Seasonal Series 35 30 2 20 15 10 N=4 5 0 2 3 4 6 7 8 Time Fig.2-9 Demand History for Example 2.7

2.8. Methods of Forecasting Trend Seasonal Series Fig. 2-9 Demand History for Example 2.7 N=4

2.8.Methods of Forecasting Trend Seasonal Series Procedures: Draw the demand curves and estimate the season length N: Computer the moving average MA(N); Centralize the moving averages; Get the centralized MA values back on period; Calculate seasonal factors,and make sure of >c,=N Divide each observation by the appropriate seasonal factor to obtain the deseasonalized demand .Forecast is made based on deseasonalized demand. Final forecast is obtained by multiplying the forecast(with no seasonality)with seasonal factors

2.8. Methods of Forecasting Trend Seasonal Series Procedures: • Draw the demand curves and estimate the season length N; • Computer the moving average MA(N); • Centralize the moving averages; • Get the centralized MA values back on period; • Calculate seasonal factors, and make sure of  ct=N. • Divide each observation by the appropriate seasonal factor to obtain the deseasonalized demand • Forecast is made based on deseasonalized demand. • Final forecast is obtained by multiplying the forecast (with no seasonality) with seasonal factors

⑧ 2.8.Methods of Forecasting Trend Seasonal Series Period Demand MA(4) 1 10 2 20 3 26 4 17 5 12 18.25 6 23 18.75 7 30 19.5 8 22 20.5 21.75

Period Demand MA(4) 1 10 2 20 3 26 4 17 5 12 18.25 6 23 18.75 7 30 19.5 8 22 20.5 21.75 2.8. Methods of Forecasting Trend Seasonal Series

2.8.Methods of Forecasting Trend Seasonal Series Procedures: Draw the demand curves and estimate the season length N: Computer the moving average MA(N); Centralize the moving averages; Get the centralized MA values back on period; Calculate seasonal factors,and make sure of >c,=N Divide each observation by the appropriate seasonal factor to obtain the deseasonalized demand .Forecast is made based on deseasonalized demand. Final forecast is obtained by multiplying the forecast(with no seasonality)with seasonal factors

2.8. Methods of Forecasting Trend Seasonal Series Procedures: • Draw the demand curves and estimate the season length N; • Computer the moving average MA(N); • Centralize the moving averages; • Get the centralized MA values back on period; • Calculate seasonal factors, and make sure of  ct=N. • Divide each observation by the appropriate seasonal factor to obtain the deseasonalized demand • Forecast is made based on deseasonalized demand. • Final forecast is obtained by multiplying the forecast (with no seasonality) with seasonal factors