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

Standard

Harvard

APA

Vancouver

Bibtex

@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

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 -

Recently viewed

Publications

  1. Mapping Complexity in Environmental Governance
  2. General Patterns and Conclusions
  3. Investigating Internal CSR Communication: Building a Theoretical Framework
  4. Dynamic capabilities and routinization
  5. Metaheuristics approach for solving personalized crew rostering problem in public bus transit
  6. Model-based nonlinear filter design for tower load reduction of wind power plants with active power control capability
  7. PD/PID-switching control as a human-machine interface for a semi-autonomous driver in automobiles
  8. Getting down to specifics on RCA [Resource Consumption Accounting]
  9. Dynamic control of internal force for visco-elastic contact grasps
  10. Analysis of a phase‐field finite element implementation for precipitation
  11. Inside-sediment partitioning of PAH, PCB and organochlorine compounds and inferences on sampling and normalization methods
  12. Differenz, Differenzierung
  13. Comparing Web-Based and Blended Training for Coping With Challenges of Flexible Work Designs
  14. Design of Reliable Remobilisation Finger Implants with Geometry Elements of a Triple Periodic Minimal Surface Structure via Additive Manufacturing of Silicon Nitride
  15. A Multilevel CFA–MTMM Approach for Multisource Feedback Instruments
  16. Cost effectiveness of guided Internet-based interventions for depression in comparison with control conditions
  17. Walk counts, labyrinthicity, and complexity of acyclic and cyclic graphs and molecules.
  18. High resolution measurement of physical variables change for INS
  19. Deterministic Pod Repositioning in Robotic Mobile Fulfillment Systems
  20. Embracing scale-dependence to achieve a deeper understanding of biodiversity and its change across communities
  21. No Concept of form within Sight Can System Theory help us?
  22. Self-perceived quality of life predicts mortality risk better than a multi-biomarker panel, but the combination of both does best
  23. Contextualizing the relationship between self-commitment and performance
  24. Turning Good Intentions Into Actions by Using the Health Action Process Approach to Predict Adherence to Internet-Based Depression Prevention
  25. Comparison of Supervised versus Self-Administered Stretching on Bench Press Maximal Strength and Force Development