Models for Time Series and ForecastingPPT
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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
© 2002 The Wadsworth Group
? = b0 + b1x y • Linear: ? = b + b x + b x2 • Quadratic: y 0 1 2
Trend Equations
? = the trend line estimate of y y x = time period b0, b1, and b2 are coefficients that are selected to minimize the deviations between the ? and the actual data values trend estimates y y for the past time periods. Regression methods are used to determine the best values for the coefficients.
• Trend equation • Moving average • Exponential smoothing
ቤተ መጻሕፍቲ ባይዱ
• 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 = 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
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.
© 2002 The Wadsworth Group
Chapter 18 - Key Terms
• Time series • Classical time series model
– – – – Trend value Cyclical component Seasonal component Irregular component
© 2002 The Wadsworth Group
• Smoothing techniques - dampen the impacts of fluctuation in a time series, thereby providing a better view of the trend and (possibly) the cyclical components. • Moving average - a technique that replaces a data value with the average of that data value and neighboring data values. • Exponential smoothing - a technique that replaces a data value with a weighted average of the actual data value and the value resulting from exponential smoothing for the previous time period.
to accompany
Introduction to Business Statistics
fourth edition, by Ronald M. Weiers
Presentation by Priscilla Chaffe-Stengel
© 2002 The Wadsworth Group
? = b0 + b1x y • Linear: ? = b + b x + b x2 • Quadratic: y 0 1 2
Trend Equations
? = the trend line estimate of y y x = time period b0, b1, and b2 are coefficients that are selected to minimize the deviations between the ? and the actual data values trend estimates y y for the past time periods. Regression methods are used to determine the best values for the coefficients.
• Trend equation • Moving average • Exponential smoothing
ቤተ መጻሕፍቲ ባይዱ
• 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 = 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
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.
© 2002 The Wadsworth Group
Chapter 18 - Key Terms
• Time series • Classical time series model
– – – – Trend value Cyclical component Seasonal component Irregular component
© 2002 The Wadsworth Group
• Smoothing techniques - dampen the impacts of fluctuation in a time series, thereby providing a better view of the trend and (possibly) the cyclical components. • Moving average - a technique that replaces a data value with the average of that data value and neighboring data values. • Exponential smoothing - a technique that replaces a data value with a weighted average of the actual data value and the value resulting from exponential smoothing for the previous time period.