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Any examples of a deep learning time series binary classification?

RepletionRepletion Member Posts: 24 image Learner III
in Help
Hello!
Im looking for an example of an deep learning time series binary classification. It would greatly help my understanding of the architecture behind them, so if anybody got an example or workflow that they are willing to share it would be much appriciated!
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  • lionelderkrikorlionelderkrikor RapidMiner Certified Analyst, Member Posts: 1,195 image Unicorn
    Hi @Repletion,

    You can go to (in RapidMiner repository) : 
    Samples -> Deep Learning -> 02 sequential data -> 02 ICU mortality classification

    Is it what you are looking for ??

    Regards,

    Lionel

  • RepletionRepletion Member Posts: 24 image Learner III
    @lionelderkrikor to some extent yes this is what im looking for. However that dataset doesnt have an ID class and when I try and replicate it with a dataset that contains an integer (label), a date (id) and various attributes, it simply gives me the following log output: "Couldn't update network in epoch n" (n=1,2,3 ...).

    Also isnt the amount of neurons in the network supposed to be equal to the amount of attributes? Or how does that exactly work (because in my head every neuron is the weights and biases for an attribute).

  • lionelderkrikorlionelderkrikor RapidMiner Certified Analyst, Member Posts: 1,195 image Unicorn
    @Repletion,

    "to some extent yes this is what im looking for..."
    Could you share your data and explain exactly what you want to perform (or predict). So it will be easier to help you...

    Regards,  
    Lionel
  • RepletionRepletion Member Posts: 24 image Learner III
    @lionelderkrikor im trying to predict if a closing price is higher than the previous days closing price (1) if not (0). It should be pretty straight forward regarding building a binary model, however Rapidminer is different from the statistics software I have experience with and I guess it shows.

    Ignore DIA Basics. Its the wrong csv, DIA filtered is the one im working with.

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  • lionelderkrikorlionelderkrikor RapidMiner Certified Analyst, Member Posts: 1,195 image Unicorn
    @Repletion

    I will study your use case.
    But after seeing your process and the description of what you want to perform, maybe you can use the following templates : 

    04 S&P 500 Regression using Windowing and Convolution
    03 gas price change regression

    Maybe you can adapt these 2 regression processes into a binary classification process

    Regards,

    Lionel



  • RepletionRepletion Member Posts: 24 image Learner III
    @hughesfleming68 drawing inspiration from that workflow I adapted mine to look like it, with some minor exceptions. Im still using the tensor based deep learning. The model trains and tests, however it runs into a classical statistical modelling issue, it simply predicts everything as down or up depending on the amount of epochs. How does one get around this problem, is there an exception that can be included in the model telling it, it cant only predict everything as the same value?
  • hughesfleming68hughesfleming68 Member Posts: 323 image Unicorn
    @Repletion. Take a look at this article. It will help you with your data prep.The same concepts apply in Rapidminer.
    https://machinelearningmastery.com/how-to-scale-data-for-long-short-term-memory-networks-in-python/
  • hughesfleming68hughesfleming68 Member Posts: 323 image Unicorn
    Also don't assume that LSTM is the best choice for your data. I always prefer CNN and don't overlook Gradient boosted trees for a problem like this.
  • RepletionRepletion Member Posts: 24 image Learner III
    edited June 2020
    @hughesfleming68 ok, I will take a look. Whats your argument to go with a CNN over an LSTM? In the financial markets certain patterns reoccur and those are the ones that an LSTM should be able to take into consideration. So im curious as to how a CNN will perform on this.
  • hughesfleming68hughesfleming68 Member Posts: 323 image Unicorn
    edited June 2020
    @Repletion. This....https://medium.com/@raushan2807/temporal-convolutional-networks-bfea16e6d7d2

    Don't underestimate the training time advantage.
  • RepletionRepletion Member Posts: 24 image Learner III
    @hughesfleming68 unfortunately it doesnt look like that there are any TCN layers or extensions in Rapidminer. 
  • RepletionRepletion Member Posts: 24 image Learner III
    @hughesfleming68When working with Gradient boosted trees and hypertuning its parameters through the nested "optimize parameters (evolutionary)" do you have any golden rules that can help minimize the process time? Say that you know that the maximum number of trees "golden rule" is between 1-500 and so on with the rest of the parameters. It would greatly help setting some boundaries for the optimizer to help cut off some of the processing time.
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