[读者文库]Introduction to Time Series Analysis and Forecasting(using R)

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Series: Wiley Series in Probability and Statistics
Hardcover: 672 pages
Publisher: Wiley; 2 edition (May 4, 2015)
Author: Douglas Montgomery
Language: English
ISBN-10: 1118745116
ISBN-13: 978-1118745113

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关键词：introduction Forecasting Time Series troduction Analysis

前段时间刚入手此书，感觉还是灰常赞的！

1. Example 3.3 For this example we will use the “lm” function.
   
2. satisfaction2.fit<-lm(Satisfaction ~ Age+Severity+Age:Severity+I(Ageˆ2)+
   
3. I(Severityˆ2), data=patsat)
   
4. summary(satisfaction2.fit)

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Example 3.12

Different packages such as car, lmtest, and bstats offer functions for Durbin–Watson test. We will use function “dwt” in package car. Note that dwt function allows for two-sided or one-sided tests. As in the example we will test for positive autocorrelation. The data are given in softsales.data where the columns are Year, Sales, Expenditures and Population (to be used in the next example).

1. library(car)
   
2. soft1.fit<-lm(Sales ~ Expenditures, data=softsales.data)
   
3. dwt(soft1.fit, alternative=”positive”)

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Example 3.13
We repeat Example 3.12 with the model expanded to include Population as well.

1. soft2.fit<-lm(Sales ~ Expenditures+Population, data=softsales.data)dwt(soft2.fit, alternative=”positive”)

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As concluded in the example, adding the input variable Population seems to resolve the autocorrelation issue resulting in large p-value for the test for autocorrelation.