# Could somebody explain to me what this ARIMA model output says?

I've asked a few questions here before regarding my thesis. Although I try my best to follow-up on your suggestions, my statistical knowledge is limited but I try my utmost. Adding a predictive model to your thesis is not required (since it isn't taught during the studies) but my thesis coach insists. So I've just let SPSS dictate the best-fitting ARIMA model for my thesis.

Basically, I have taken some internet data (hbVol0LN is number of tweets, hbBullQuality0 is the ratio for postive against negative tweets, etc.) for 100 companies over 103 days. Here, the dependent variable is the return of the stock of each of those 100 companies per day. I already performed an OLS (although it has been pointed out that this is not the ideal model for my research, it is accepted by my coach), but now I believe this ARIMA model should hold the predictive value of the data.

It is very hard to find annotated ARIMA output online, or a paper which describes the output in a way I can understand. Could you perhaps give me some insights of what this output is telling me? Any help at all is greatly appreciated.

If you're having a hard time reading the graph, here's the full-size one: http://i.stack.imgur.com/H42YP.png

Again, I cannot express how frustrating it is for somebody who has hardly had to do any statistics during his studies, having to produce a predictive model. Therefore, really, any help is appreciated.

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What I'd advise you is to read SPSS tutorial (found under Help menu), time series chapter. –  ttnphns Aug 20 '12 at 20:51
@ttnphns That suggestion is not very helpful. You should consider the fact that Pr0no is having trouble understanding time series model fitting and is a bit panicked. A simple explanation like what I give below seems to me to be more appropriate. –  Michael Chernick Aug 20 '12 at 21:09
I don't think so. If the OP would deign to read through "Forecasting option" in Case Studies submenu there, the misunderstanding (showing, in particularly, in comments to your good answer) would probably go away. –  ttnphns Aug 20 '12 at 22:05
@ttnphns My interpretation of MAPE = 170,905 and MaxAPE = 12045,319 (Model Fit table) come from the SPSS tutorial (which I already went through)...but it didn't answer my questions. I'm confused how as to how R-squared can be fairly reasonable at 0.286, while the uncertainty of the prediction (as explained in the SPSS help menu) seems to be, as said, 12045%? I don't understand, since in the example given in the SPSS help files, it's a neat 3%. –  Pr0no Aug 20 '12 at 22:12
@Pr0no, the R-squared is too low for time series modeling. Normally, it would be .70 or higher. So there's no wonder that MAPE is high (MAPE can be >100% which then means that your error, or magnitude of residual, most of the time is greater than the actual value you're predicting). The model (0,0,17) looks unnatural: 17-order MA-parameter! Almost just a noise. Your model is bad and predicts badly. There must be something you are missing when you do input and specifications in the Modeler –  ttnphns Aug 20 '12 at 22:49

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