Any examples of a deep learning time series binary classification?
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!
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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@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).
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).
@Repletion,
Regards,
Lionel
"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
@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.
Ignore DIA Basics. Its the wrong csv, DIA filtered is the one im working with.
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@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
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
@Repletion, could you download the value series extension. There are a coupe of tools there that I use to make binomial classification really quick. Then take a look at the attached process.
@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?
@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/
https://machinelearningmastery.com/how-to-scale-data-for-long-short-term-memory-networks-in-python/
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.
@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.
@Repletion. This....https://medium.com/@raushan2807/temporal-convolutional-networks-bfea16e6d7d2
Don't underestimate the training time advantage.
Don't underestimate the training time advantage.
@hughesfleming68 unfortunately it doesnt look like that there are any TCN layers or extensions in Rapidminer.
@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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@Repletion, could you download the value series extension. There are a coupe of tools there that I use to make binomial classification really quick. Then take a look at the attached process.
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