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I want to make a LSTM RNN for timeseries prediction, but some of my predictors are monthly and others are daily. Any advice / examples on how to set up this network?

The frequency of the predictions is monthly.

Thanks.

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    $\begingroup$ What is the frequency of the predictions? $\endgroup$
    – Aaron
    Commented May 4, 2016 at 21:08
  • $\begingroup$ Thanks for Q. Monthly. I have adjusted the original question. $\endgroup$
    – BHP
    Commented May 4, 2016 at 21:39
  • $\begingroup$ Another descriptor is 'misaligned' time series. $\endgroup$ Commented Aug 10, 2017 at 18:04

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You can maybe get some inspiration from the ideas presented in this article which proposes a representation of the time series such that it deals with asynchronous sampling: you encode what is the source (the id of the time series) and the duration (time to the last value considering all time series) of the current value, and you end up with a single time series of values (as pictured in Figure 1 of the article that I attached below).

enter image description here

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You could use a hierarchical structure. One LSTM can create an embedding vector for the sequence of daily predictors for each month. Then this embedding is fed into a second LSTM along with the monthly predictor variables.

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  • $\begingroup$ Is this possible with current libraries? @Aaron $\endgroup$ Commented Aug 10, 2017 at 18:06
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    $\begingroup$ Actually, check out the PhasedLSTM in the latest version of tensorflow $\endgroup$
    – Aaron
    Commented Aug 12, 2017 at 7:25
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Check the power demand experiments in this paper: https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2015-56.pdf. Different layers in a deep network can capture different time scales.

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    $\begingroup$ Could you make your answer a bit more explicit so that one would not need to read the linked paper to get the idea? $\endgroup$ Commented Jul 10, 2016 at 17:04

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