As the title says, I want to train a Jordan network (i.e. a particular kind of recurrent neural network) using a certain number of time series.

Let's say that $x_1, x_2, \ldots x_N$ are $N$ input time series (i.e. $x_i = [x_{i,1}, x_{i,2}, \ldots, x_{i,T}]$, where $T$ is the length of the time series) and $y_1, y_2, \ldots y_N$ (i.e. $x_i = [y_{i,1}, y_{i,2}, \ldots, y_{i,T}]$) are the corresponding target time series.

More specifically, the target time series are just sequences of "$0$s", which may end with sequences of "$1$"s. Here I show you some example:

$$y_i = [0 ~ 0 ~ 0 \ldots 0 ~ 0 ~ 1 ~ 1 ~ 1 \ldots 1 ~1 ], $$ $$y_i = [0 ~ 0 ~ 0 \ldots 0 ~ 0]. $$

This means that I want that my machine "learn to raise" under some situations related to the corresponding inputs $x_i$. Indeed, the objective of my network is to "raise" an alarm if "something" happens.

At the moment, my training strategy is the following. I create a new time series which corresponds the concatenation of all the available $x_i$ and $y_i$. Let's call the concatenated series $X$ and $Y$. Then I use $X$ and $Y$ to train a network.

Here is my problem. If I concatenate, then I also teach to my machine to "drop", since I can have situation like this:

$$Y = [ \ldots 1 ~ 1 ~ 0 ~ 0 \ldots].$$

Is this really a problem? Are there other "training strategies" to be employed so that I avoid this kind of unwanted behaviors?

  • $\begingroup$ My first thought when reading this was that the issue would be with the Xs instead. By concatenating the Xs, you are artificially including a transition from x_{i,T} to x_{j, 1}. If this transition does actually happen in your data, you might get some interference so its worth exploring your data for that. Otherwise, I think that this approach is fine. $\endgroup$ – Jaden Travnik Nov 14 '18 at 14:13
  • $\begingroup$ @JadenTravnik you are right, thanks. Anyway, I'm trying to train a neural network since it is very difficult to assess if there is a real transition in the data $x$. Indeed, data $x$ are multidimensional. If there was a "graphical evidence", then maybe machine learning would have been unnecessary. $\endgroup$ – the_candyman Nov 14 '18 at 18:50
  • $\begingroup$ Why a downvote? I think it is stupid to downvote without commenting. $\endgroup$ – the_candyman Jan 11 '19 at 23:56

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