Investigating the Effect of Noise Elimination on LSTM Models for Financial Markets Prediction Using Kalman Filter and Wavelet Transform
Publikation: Beiträge in Zeitschriften › Zeitschriftenaufsätze › Forschung › begutachtet
Standard
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, Jahrgang 19, 39, 2022, S. 432-441.Publikation: Beiträge in Zeitschriften › Zeitschriftenaufsätze › Forschung › begutachtet
Harvard
APA
Vancouver
Bibtex
}
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 -