I have 6 sequences (time series); they all belong to the same variable. I divide each sequence in two parts having 80% and leaving the last 20% for validation. I am doing the analysis and modelling in Matlab.

In the training set (80%):
I want to train on 5 sequences using an autoregressive method having a window size of 1 event in order to model the behaviour of the sequences (e.g. sequences 2:5 = $X$) and see whether I can predict the one that is left out (e.g. sequence 1 = $Y$).

I want to have the model learning by steps. The dependency would be like $$ Y_n = \text{sd}(X_{n-1}) + \text{mean}(X_{n-1}) $$

For this I am thinking of using the Matlab function arima to estimate the model parameters. I don’t know though how to integrate the modelling of $X$ in arima since the estimation seems to be for $Y$.
Q1: Maybe I am not understanding well how to use this?

I assume an alternative would be using fitlm to obtain the model between the mean and standard deviation (of all the sequences in the training set) per event in $X_{n-1}$ and the sequence in $Y_n$.

Q2: Which of these both methods would be better to take into account the changes from event to event?

In the testing set (20%):
Once I have the model I would like to use it to test its accuracy on the 20% percent left. I understand that when using fitlm the best would be to use predict or feval and for arima I should use forecast.
Q3: Is this correct?

Q4: Can anyone point me some simple example or tutorial on how to do this correctly?


1 Answer 1


This is not an autoregressive model. Autoregressive series would have $X_n(t)=\dots \theta_1 X_n(t-1)$ terms, while yours seem to have only $X_n(t)=\dots \theta_1 X_{k\ne n}(t-1)$.

Hence, yours is a simple cross-sectional model. Here how it looks: $$y=\beta_0+\beta_1 x_1 + \beta_2 x_2$$

All you need is to create two new variables: $x_1,x_2$. The first one is the StDev of the other four (five?) series, and the second one is the mean. So, you have

y = x(2:end,1)
temp = x(1:end-1,2:5);
X = [std(temp,0,2) mean(temp,0,2)]
mdl = fitlm(y,X)

That's it. Once you have the model object you can use its predict function and other methods to get what you want. Read fitlm's help, it has examples.

  • $\begingroup$ Thanks, I explained something wrong I believe. In my case it would be: X = [std(temp') mean(temp')] $\endgroup$
    – Noque
    Feb 3, 2016 at 21:28
  • $\begingroup$ @Noque my bad, I fixed the code. Your explanation was not incorrect. What you need is a std or mean across the sequences, my code had a bug. $\endgroup$
    – Aksakal
    Feb 3, 2016 at 22:42
  • $\begingroup$ Thanks, I think there is another bug fitlm should have the arguments reversed. That is fitlm(X,y) $\endgroup$
    – Noque
    Feb 4, 2016 at 15:22
  • $\begingroup$ @Noque, sorry, you might be right, I don't remember exact syntax $\endgroup$
    – Aksakal
    Feb 4, 2016 at 15:46

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