Cesar Chew at 16:52, 25 November 2014

2014-11-25T16:52:37Z

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{{Cat|Forecasting}}

== Moving Average ==
; Smoothing with Moving Average
<source lang = "rsplus">
plot(ma(rawdata$Price,order=2),type="l")
lines(ma(rawdata$Price,order=4),type="l",col="blue")
lines(ma(rawdata$Price,order=12),type="l",col="red")
</source>

== Autocorrelation Function ==
<source lang = "rsplus">
acf(rawdata$Price)
</source>

== Seasonality ==
<source lang = "rsplus">
price.ts <- ts(rawdata$Price,frequency=12)
seasonplot(price.ts)
</source>

== Autoregression ==

* AR(''p'') - autoregressive model of order ''p''
* <math>\varphi_1, \ldots, \varphi_p</math> - '''''parameters''''' of the model
* <math>c</math> a constant
* <math>\varepsilon_t</math> white noise

:<math> X_t = c + \sum_{i=1}^p \varphi_i X_{t-i}+ \varepsilon_t \,</math>

<source lang="rsplus">
rawdata <- read.table("http://training-course-material.com/images/1/19/Sales-time-series.txt",h=T)
rawdata$Date <- as.Date(rawdata$Date)

plot(rawdata$Price,type="l",xlim=c(1,200))

# Build a model (try different methods)
model <- ar(rawdata$Price)

# Predict
pr = predict(model, n.ahead=100)

# add prediction to the plot
lines(pr$pred,col="red")

# Increase the order
model <- ar(rawdata$Price,order.max=40,aic=FALSE)
pr = predict(model, n.ahead=100)
lines(pr$pred,col="blue")

# Change the method
model <- ar(rawdata$Price,order.max=40,aic=FALSE,method="burg")
pr = predict(model, n.ahead=100)
lines(pr$pred,col="green")

</source>

== See Also ==
* http://maths-people.anu.edu.au/~johnm/courses/r/ASC2008/pdf/Rtimeseries-ohp.pdf

R - Forecasting with ARIMA - Revision history

Cesar Chew at 16:52, 25 November 2014