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I'm attempting to speed up a slow auto.arima function by running it on a computer with 4 dual-core CPUs (I'm using Ubuntu 13.04 and R 2.15.2). The function is fitting a time series with 350,000 data points and roughly 50 exogenous variables. I'm using the below code
fit<-auto.arima(orders,xreg=exogen, stepwise=FALSE, parallel=TRUE, num.cores=4)
However, I have multiple CPUs (each with multiple cores), not just one CPU with multiple cores. In case R was smart enough to get around this cores/CPUs differentiation, I took a look at my resource monitor and saw this:
which shows that only CPU3 is maxed out.
Any thoughts on how to resolve? Does the forecast package work with DoSNOW?
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Auto. arima has the ability to decide whether or not the data used to train the model needs a seasonal differencing.
💾 ARIMA requires a long historical horizon, especially for seasonal products. Using three years of historical demand is likely not to be enough. Short Life-Cycle Products. Products with a short life-cycle won't benefit from this much data.
1- Check again the stationarity of the time series using augmented Dickey-Fuller (ADF) test. 2- Try to increase the number of predictors ( independent variables). 3- Try to increase the sample size (in case of monthly data, to use at least 4 years data.
Try num.cores=8 and num.cores=7, use system.time() to see which one runs faster. If I remember correctly, R treats 1 core as one CPU. You have 8 cores, if I understood you correctly: "4 dual-core CPUs".
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