I suspect that, similar to other time series applications, you'll find some interesting signals from exogenous effects. I wonder how LSTMs can incorporate this exogenous information for time series analysis.
I.e. I can build a look up table looking at the last k data points predicting the next and you're going to need a massive training dataset to make that not work well on the train set (something like O(exp(k)*k/N) time series).
It's more than just compressing the training set, a human can use a training set to learn seasonality, volatility, streak durations, moving averages, etc. which are learnings that can be used to infer future movement. The LTSM is learning it's own observations to predict the next tick.
Sure, you can prefer to use subsets of a single time series instead of multiple time series. The issue remains that it doesn't matter what your performance on training data is. You still need to partition your dataset into training and test data - otherwise you could just be storing a lookup table for all you know. It looks like the author has trained on the entirety of the dataset, and then is just considering that performance...
Let me put this another way. You do this with a random walk. Train on your entire timeseries - every length 50 window. Say that there's only 8 unique values at each timestep. That means that there's 8^50 possible input sequences into the neural network. A sufficiently complex neural network can fit an arbitrary function, so if you just have a couple thousand windows there's ~(8^50 / 1000) possible functions that can predict the correct output exactly - and this is on noise! And in all likelihood the neural net will learn that noise: https://arxiv.org/abs/1611.03530 Without comparing training and test results there's no way to know that neural network learned anything of value - it can get perfect accuracy on training data that's pure noise!
This stuff is really critical to get right if you're doing machine learning.
>A stock time series is unfortunately not a function that can be mapped.
Not sure about "predicting" the stocks with ANNs, but how do you explain the Medallion Fund averaging about 30%/y (ballpark) without a single loss year since 1990 ?
https://www.bloomberg.com/news/articles/2016-11-21/how-renai...
https://www.bloomberg.com/news/articles/2015-02-20/high-freq...
I wonder what the Sharpe Ratios of the trading systems these funds run are.
https://www.youtube.com/watch?v=hKcOkWzj0_s
Jane Street went from zero to trading $1 trillion in volume in just 15 years.
Anyway thanks
