Improving modeling of stochastic processes by smart denoising
This paper proposes a novel method for modeling stochastic processes, which are known
to be notoriously hard to predict accurately. State of the art methods quickly overfit
and create big differences between train and test datasets. We present a method based
on smart noise addition to the data obtained from unknown stochastic process, which
is capable of reducing data overfitting. The proposed method works as an addition to
the current state of the art methods in both supervised and unsupervised setting. We
evaluate the method on equities and cryptocurrency datasets, specifically chosen for
their chaotic and unpredictable nature. We show that with our method we significantly
reduce overfitting and increase performance, compared to several commonly used machine
learning algorithms: Random forest, General linear model and LSTM deep learning model.
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