Ch18时间列模型与预测.ppt

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Ch18时间列模型与预测

CHAPTER 18 Models for Time Series and Forecasting to accompany Introduction to Business Statistics fourth edition, by Ronald M. Weiers Presentation by Priscilla Chaffe-Stengel Donald N. Stengel ? 2002 The Wadsworth Group Chapter 18 - Learning Objectives Describe the trend, cyclical, seasonal, and irregular components of the time series model. Fit a linear or quadratic trend equation to a time series. Smooth a time series with the centered moving average and exponential smoothing techniques. Determine seasonal indexes and use them to compensate for the seasonal effects in a time series. Use the trend extrapolation and exponential smoothing forecast methods to estimate a future value. Use MAD and MSE criteria to compare how well equations fit data. Use index numbers to compare business or economic measures over time. Chapter 18 - Key Terms Time series Classical time series model Trend value Cyclical component Seasonal component Irregular component Trend equation Moving average Exponential smoothing Seasonal index Ratio to moving average method Deseasonalizing MAD criterion MSE criterion Constructing an index using the CPI Shifting the base of an index Classical Time Series Model y = T ? C ? S ? I where y = observed value of the time series variable T = trend component, which reflects the general tendency of the time series without fluctuations C = cyclical component, which reflects systematic fluctuations that are not calendar-related, such as business cycles S = seasonal component, which reflects systematic fluctuations that are calendar-related, such as the day of the week or the month of the year I = irregular component, which reflects fluctuations that are not systematic Trend Equations Linear: = b0 + b1x Quadratic: = b0 + b1x + b2x2 = the trend line estimate of y x = time period b0, b1, and b2 are coefficients that are selected to minimize the devia

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