I am building an LSTM that takes in time-series financial data. My dataset is made up of IDs (each ID is a certain stock), and timestamps. For each ID at each timestamp, there are a number of features and a label.

My LSTM takes in as input batches of shape (50, 25, 108). The 50 corresponds to 50 different IDs, 25 corresponds to 25 timestamps for each of these IDs, all over the same parallel 25 points in time and the 108 corresponds to the 108 features I have for each ID at each timestamp which can influence its price. Each timestamp also has a label, y. For each of these 50 sequences of 25 timestamps, my LSTM is is trying to predict the value of y at this 25th timestamp - the last label of the sequence.

So, in short, my network takes in 50 different sequences across the same 25 points in time, and so the RNN is unrolled over 25 time steps and uses the output of the last as its prediction.

For the sake of simplicity, say my training dataset consists of 9 IDs and 25 timestamps and instead of my LSTM taking input of shape [50, 25, 108] it is instead [3, 5, 108]. Therefore, I would batch this data and pass it through my LSTM as 3 IDs over 5 timestamps (i.e each batch would be 3 sequences spanning the same 5 timestamps parallel in time). So the first batch would be IDs 0, 1 and 2, each over timestamps 0, 1, 2, 3, 4. The next batch would be the same IDs over timestamps 5, 6, 7, 8, 9. I would pass through 5 of these batches so that all 25 timestamps for these 3 IDs have gone through the network. It is at this point that I would reset the state of the LSTM, and then pass in 5 batches across IDs 3, 4, and 5.

Is this the correct thinking of when to reset the LSTM's state - at the beginning of each time series? My thinking is that for each batch along the 25 timestamps for a given ID, the LSTM's memory is valuable as these timestamps proceed the timestamps of the previous batch. However, when I then move to timestamp 0 of a new set of IDs, as this is the first timestamp in my data, the LSTM cannot use its memory from previous points in time to influence its predictions, and so the state of the LSTM is reset.

Secondly, once my LSTM has been trained and I am now testing its accuracy on unseen data without tuning and optimising its parameters, do I still use the same logic behind resetting the state? Do I continue to reset it each time I pass in the first timestamp of an ID? Or should resetting only be performed when training?

  • $\begingroup$ You can improve this question by explaining your predictor/response data in standard language. For example, you could say that you have $n$ observations of $p$ stock returns. This data can then be arranged into an n-by-p matrix $X$ which can be fed into the LSTM, provided a response vector $Y$. You should also tell us a little more about what you are actually trying to do/predict. Also, your particular method of pooling different stocks' data does not quite make sense to me, and I don't understand why you'd use such a tiny batch size (three). A little more info on that would help as well. $\endgroup$ – Josh Oct 13 '17 at 15:38
  • $\begingroup$ @Josh I have added further details to my post. $\endgroup$ – KOB Oct 13 '17 at 15:48
  • $\begingroup$ So the idea is that all 50 IDs share a similar predictor-response relationship? Then it doesn't actually matter that you have multiple IDs; basically, you have some number of predictor time series and a single response value for each series. So you want the model to train on each sequence separately; by that I mean that from one sequence to another the weights will update, but the state must be reset. When predicting, you should reset the state to zero at the beginning of each sequence since you've already decided that the response depends only on the 25 values provided. $\endgroup$ – Josh Oct 13 '17 at 16:03
  • $\begingroup$ @Josh so my thinking is correct that I process an ID across all of its timestamps, and then reset the state when moving onto a new ID? $\endgroup$ – KOB Oct 13 '17 at 16:06
  • $\begingroup$ No, I don't think that's right. Your conjecture is that some combination of the 25 predictor observations from a particular sequence (and not the observations from other sequences) has a relationship to the single response observation, correct? If that's so, then you don't want the network to "remember" its state (not to be confused with the weights) from one sequence to the next, and you'd want to reset it after training on each sequence. Otherwise, the short term memory (state) evolves such that information from previous sequences will affect predictions on the next sequence. $\endgroup$ – Josh Oct 13 '17 at 16:29

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