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

2022 ◽  
Vol 19 ◽  
pp. 432-441
Author(s):  
Amin Karimi Dastgerdi ◽  
Paolo Mercorelli
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Kalman Filter ◽  
Wavelet Transform ◽  
Financial Markets ◽  
Financial Time Series ◽  
Considerable Effect ◽  
Noise Elimination ◽  
Financial Time ◽  
Learning Techniques

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.

scholarly journals Deep Learning for Financial Time-Series Data Analytics: An Image Processing Based Approach

2020 ◽  
Vol 10 (1) ◽  
pp. 51-56
Author(s):  
Watthana Pongsena ◽  
Prakaidoy Ditsayabut ◽  
Nittaya Kerdprasop ◽  
Kittisak Kerdprasop
Keyword(s):  
Image Processing ◽  
Time Series ◽  
Deep Learning ◽  
Data Analytics ◽  
Time Series Data ◽  
Financial Time Series ◽  
Series Data ◽  
Financial Time

scholarly journals Nonlinear Autoregressive Neural Network and Extended Kalman Filters for Prediction of Financial Time Series

2020 ◽  
Vol 2020 ◽  
pp. 1-6 ◽  
Author(s):  
Ghassane Benrhmach ◽  
Khalil Namir ◽  
Abdelwahed Namir ◽  
Jamal Bouyaghroumni
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Financial Time Series ◽  
Broad Class ◽  
Financial Time ◽  
The Neural Network ◽  
Nonlinear Autoregressive ◽  
Autoregressive Neural Network

Time series analysis and prediction are major scientific challenges that find their applications in fields as diverse as finance, biology, economics, meteorology, and so on. Obtaining the method with the least prediction error is one of the difficult problems of financial market and investment analysts. State space modelling is an efficient and flexible method for statistical inference of a broad class of time series and other data. The neural network is an important tool for analyzing time series especially when it is nonlinear and nonstationary. Essential tools for the study of Box-Jenkins methodology, neural networks, and extended Kalman filter were put together. We examine the use of the nonlinear autoregressive neural network method as a prediction technique for financial time series and the application of the extended Kalman filter algorithm to improve the accuracy of the model. As application on a real example, we are analyzing the time series of the daily price of steel over a 790-day period for establishing the superiority of this method over other existing methods. The simulation results using MATLAB and R software show that the model is capable of producing a reasonable accuracy.

Prediction research of financial time series based on deep learning

2020 ◽  
Vol 24 (11) ◽  
pp. 8295-8312 ◽  
Author(s):  
Zhaoyi Xu ◽  
Jia Zhang ◽  
Junyao Wang ◽  
Zhiming Xu
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Financial Time Series ◽  
Financial Time
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