Solved – ARIMA forecasting book data sets R

arimadatasetforecastingreferences

I recently bought a fantastic phenomenal book on ARIMA – "Forecasting with Univariate Box – Jenkins Models: Concepts and Cases" by Alan Pankratz. It was originally published in 1983 therefore it does not have the datasets associated with the data sets for practice. Are there any place that someone could recommend for getting the data sets for this book. Are there any packages in R that has time series forecasting data ?

I just read thru chapter 4, I cannot stop reading this book. This is one of the best forecasting book that I own besides Forecasting: Methods and Applications Makridakis et al.,

The publisher is coming with a 2nd edition in 2014, hopefully they have electronic version of the data sets for practice. I would greatly appreciate if anyone could recommend a place where I can get the data.

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The data from the 8 case studies in Pankratz (1983) are available at http://datamarket.com/data/list/?q=provider%3Atsdl+pankratz.

For forecasting data in R, there are lots of packages. See http://cran.r-project.org/web/views/TimeSeries.html

If you liked the Makridakis et al (1998) book, you might like its successor at http://otexts.com/fpp/

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Solved – ARIMA continuous forecasting

ARIMA models are univariate so the only information they use is the history of the series. Essentially, they tell you how the variable will react to previous stochastic variation. When you start forecasting with them you no longer have any 'noise' in them because your model will not add in error terms (and nor should it). So what you'll see is that the forecast will quickly decay to the constant term/trend in your ARIMA model. Re-estimating the model won't help because you're not adding any new information about the underlying process.

Over 180+ forecast periods you'll need to add in more explanatory variables to get anything more than a trend line out of your model. For instance, you might include macroeconomic projections as an input, which would give you some dynamics over a 6 month period.

Update: If you are going for in-sample predictive power then using all the data will give you a better fit, so yes it will improve. The underlying question is what you want to achieve. If you're trying to understand the causative factors explaining the dependent variable then a few extra observations aren't going to overturn your theoretical priors. If you want forecasting power then you want to use as much data as possible to estimate your model. Of course you need to be wary of overfitting, which I guess is why you want to check the estimation against a sub-sampled model. If you're seeing a lot of change in your coefficients as you increase the sample size and they don't stabilise that's indicating your model's mis-specified.

Solved – ARIMA forecasting with auto.arima() and xreg

Basically what caused the issue is the line ts(data[1:96,4],start=1, end=96,frequency =7 ), when you specify both start and end with frequency = 7, R is trying multiply the series so that it has a length of 96 weeks.

Recall R defines the start and end time in seasons (weeks in your case). Since you are fitting daily data, only specifying start = 0 or start = 1 should be sufficient.

Instead of running View(saledata), try to use saledata to debug yourself and you can see wrong length of time series is outputted .

Start = c(1, 1) 
End = c(96, 1)

When you do ARIMA forecast with xreg, basically you will need to create a matrix newxreg for your next 48 days with the same structure as xreg, then specify newxreg = newxreg in the forecast function. A good habit for the xreg and newxreg matrix would be to include a Day column that acts as an ordering for the data.

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Related Solutions

Solved – ARIMA continuous forecasting

Solved – ARIMA forecasting with auto.arima() and xreg

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