Improving modeling of stochastic processes by smart denoising (Informatica, 2022)
This paper proposes a novel method for modeling stochastic processes, which are knownto be notoriously hard to predict accurately. State of the art methods quickly overfitand create big differences between train and test datasets. We present a method basedon smart noise addition to the data obtained from unknown stochastic process, whichis capable of reducing data overfitting. The proposed method works as an addition tothe current state of the art methods in both supervised and unsupervised setting. Weevaluate the method on equities and cryptocurrency datasets, specifically chosen fortheir chaotic and unpredictable nature. We show that with our method we significantlyreduce overfitting and increase performance, compared to several commonly used machinelearning algorithms: Random forest, General linear model and LSTM deep learning model.