© 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.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