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Since you want to build a binary classifier based on time-ordered tabular data, I see two possible approaches among others: as you suggest, split your dataset in ordered train-test folds, so you reproduce the "real" situation of having, at each time interval, a historic dataset to train on and a test (and later evaluation) set; you can use the ...


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In your case, you have first to deal with the biological data complexity. I don't know the minimum sampling rate to detect brain epilepsia or any brain behavior. I would recommend to study some articles to know the best practices about EEG signal analysis like this one : https://www.frontiersin.org/articles/10.3389/fneur.2020.00375/full Maybe there are good ...


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I think this problem is pretty much a standard random walk (similar to the case of stock prices). The only non-autoregressive explanatory variable are the tweets. It is more or less standard today to predict some "sentiment" from news articles etc. and to use this to predict market outcomes. So the tweets could be really helpful. You could approach ...


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Seems like Mixture density networks provide a great solution. Gaussian mixtures can be numerically compared to both the linear regression MSE loss approach and the softmax cross-entropy loss approach via negative log likelihood.


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The Kalman Filter is a model-based algorithm, meaning that you would need an equation system that describes how the values A, B, C, D evolve in time. If you do not have that, then the Kalman Filter is not suitable for you. Example To put this in context, consider that we want to predict position $x_k$. We know from high school physics that $x_k = x_{k-1}+v_k\...


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