文档介绍：CHAPTER 18 Models for Time Series and Forecasting
to pany
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 ponents 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 pensate 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 pare how well equations fit data.
Use index numbers pare business or economic measures over time.
© 2002 The Wadsworth Group
Chapter 18 - Key Terms
Time series
Classical time series model
Trend value
ponent
ponent
ponent
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
© 2002 The Wadsworth Group
Classical Time Series Model
y = T • C • S • I
where y = observed value of the time series variable
T = ponent, which reflects the general tendency of the time series without fluctuations
C = ponent, which reflects systematic fluctuations that are not calendar-related, such as business cycles
S = ponent, which reflects systematic fluctuations that are calendar-related, such as the day of the week or the month of the year
I = ponent, which reflects fluctuations that are not systematic
© 2002 The Wadsworth Group
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 deviations between the trend estimates and the actual data values y for the past time periods. Regression methods are used to determine