Investigating the Effect of Noise Elimination on LSTM Models for Financial Markets Prediction Using Kalman Filter and Wavelet Transform

Research output: Journal contributionsJournal articlesResearchpeer-review


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.

Original languageEnglish
Article number39
JournalWSEAS Transactions on Business and Economics
Pages (from-to)432-441
Number of pages10
Publication statusPublished - 2022

    Research areas

  • Deep Learning, Financial Markets, Kalman Filter, LSTM, Time-series Forecasting, Wavelet Transform
  • Engineering