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

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Investigating the Effect of Noise Elimination on LSTM Models for Financial Markets Prediction Using Kalman Filter and Wavelet Transform. / Dastgerdi, Amin Karimi; Mercorelli, Paolo.

In: WSEAS Transactions on Business and Economics, Vol. 19, 39, 2022, p. 432-441.

Research output: Journal contributionsJournal articlesResearchpeer-review

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@article{28557e5a30204c1f87ed63772ef77b68,
title = "Investigating the Effect of Noise Elimination on LSTM Models for Financial Markets Prediction Using Kalman Filter and Wavelet Transform",
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.",
keywords = "Deep Learning, Financial Markets, Kalman Filter, LSTM, Time-series Forecasting, Wavelet Transform, Engineering",
author = "Dastgerdi, {Amin Karimi} and Paolo Mercorelli",
year = "2022",
doi = "10.37394/23207.2022.19.39",
language = "English",
volume = "19",
pages = "432--441",
journal = "WSEAS Transactions on Business and Economics",
issn = "1109-9526",
publisher = "World Scientific and Engineering Academy and Society - WSEAS",

}

RIS

TY - JOUR

T1 - Investigating the Effect of Noise Elimination on LSTM Models for Financial Markets Prediction Using Kalman Filter and Wavelet Transform

AU - Dastgerdi, Amin Karimi

AU - Mercorelli, Paolo

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

KW - Deep Learning

KW - Financial Markets

KW - Kalman Filter

KW - LSTM

KW - Time-series Forecasting

KW - Wavelet Transform

KW - Engineering

UR - http://www.scopus.com/inward/record.url?scp=85125391726&partnerID=8YFLogxK

UR - https://wseas.com/journals/bae/2022.php

UR - https://wseas.com/journals/articles.php?id=2336

U2 - 10.37394/23207.2022.19.39

DO - 10.37394/23207.2022.19.39

M3 - Journal articles

AN - SCOPUS:85125391726

VL - 19

SP - 432

EP - 441

JO - WSEAS Transactions on Business and Economics

JF - WSEAS Transactions on Business and Economics

SN - 1109-9526

M1 - 39

ER -

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