Forecasting Model Selection

# Forecasting Model Selection - Weighted Moving Average or...

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TIME SERIES FORECASTING Determining the Model Type 1. Plot values of the time series (y) vs. period (x). If plot DOES APPEAR to have long term trend and seasonal components, use either a regression model with dummy variables (additive model) or classical decomposition (multiplicative model). If the plot DOES NOT APPEAR to have seasonality components, go to Step 2 . 2. Test for linear trend. Do a regression of the time series values (y) vs. the period (x). The test is: H 0 : β 1 = 0 (No trend) H 1 : β 1 0 (Linear trend) Look at the p-value for β 1 . p < α p > α Conclude: LINEAR TREND Conclude: NO LINEAR TREND Use: Regression or Holt's Use: Last Period, Moving Average,

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Unformatted text preview: Weighted Moving Average or Exponential Smoothing See Examples EXAMPLE 1 EXAMPLE 2 Does not appear to have seasonal variation. Use regression to check for long term Use regression with dummy variables or classical decomposition. Appears to have long term trend and seasonal variation Example 3 Does not appear to have seasonal variation. Use regression to check for long term Use last period, moving average, weighted moving average or exponential smoothing. High p-value for Period. Conclusion: No long term Low p-value for Period. Conclusion: Long term trend. Use regression or Holt's....
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## This note was uploaded on 08/29/2011 for the course STATISTICS 361 taught by Professor Gupta during the Spring '11 term at California State University , Monterey Bay.

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Forecasting Model Selection - Weighted Moving Average or...

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