WSEAS Transactions on Business and Economics
Print ISSN: 1109-9526, E-ISSN: 2224-2899
Volume 19, 2022
Investigating the Effect of Noise Elimination on LSTM Models for Financial Markets Prediction Using Kalman Filter and Wavelet Transform
Authors: ,
Abstract: Predicting financial markets is of particular importance for investors who intend to make the most profit. Analysing reasonable and precise strategies for predicting financial markets has a long history. Deep learning techniques include analyses and predictions that can assist scientists in discovering unknown patterns of data. In this project, application of noise elimination techniques such as Wavelet transform and Kalman filter in combination of deep learning methods were discussed for predicting financial time series. The results show employing noise elimination techniques such as Wavelet transform and Kalman filter, have considerable effect on performance of LSTM neural network in extracting hidden patterns in the financial time series and can precisely predict future actions in these markets.
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Keywords: Deep Learning, Time-series Forecasting, Financial Markets, LSTM, Wavelet Transform, Kalman Filter
Pages: 432-441
DOI: 10.37394/23207.2022.19.39