Parallel processing in ARIMA
<p>I have one month half-hourly data (48 readings per day). Using <code>auto.arima()</code> of <code>R forecast</code> package, I forecast the readings of coming day. Currently, I forecast only for 5 days. Everything works fine, but issue is with running time </p>

<pre><code>####### PARALLEL CASE ###########      
        library(doParallel); library(forecast)
Line 1  tsobject &lt;- ts(data, frequency = 48)   
Line 2  arimaObject &lt;- auto.arima(tsobject,xreg=temperature, stepwise = FALSE, parallel=TRUE,num.cores= detectCores-1) # temperature is another predictor variable
Line 3  predresult &lt;- predict(arimaObject,n.ahead = 48, newxreg =   testday_data$temperature)
######### SERIAL CASE ###########
Line 2  arimaObject &lt;- auto.arima(tsobject,xreg=temperature)
</code></pre>

<p>I used same code on two different machines and here are the running statistics</p>

<p><a href="https://i.stack.imgur.com/QEmY0.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/QEmY0.png" alt="enter image description here"></a></p>

<p>To me it seems that <code>parallel</code> option in <code>auto.arima()</code> is not working properly. Am I not able to use all the cores properly? Moreover, using the same data and same code, why it takes more time on ubuntu server as compared on MAC with 4 cores.</p>