I'm a newbie in Time Series Analyses. I'm using
ARIMA to make a prediction about my monthly data. So sorry that I cannot post my data here, you can just dump the data to use for example.
auto.arima() to build my model and
forecast to predict. Both functions are loaded from
forecast library. From my understanding, before building the model, we have to remove the trend and seasonal effect from our data, right? So, my question is: do we have to add them back to the predicted value? If we do, then how? Or the predicted values are already counted for trend and season?
Could you please show me an example with plots about this? Thank you so much for your help.
I'm adding some plots to this post.
Here is how my data look like
Then, I decompose it. From this graph, I can see that it has a seasonal effect, no trend, and large remainder. Is it bad, the remainder?
Then, I remove the seasonal effect using
seasadj() function, put the adjusted data into the
auto.arima(), and make prediction. Then, here is what I got:
After reading your answer, I try putting my original data to the
auto.arima(), and I got this prediction:
So, is everything good? Did I do anything wrong? And I think I should take the second prediction so that I don't have to put the seasonal effect back in? Am I right?