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

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 Comparative Analysis of Deep Learning Models for Multi-Step Prediction of Financial Time Series

2020 ◽  
Vol 16 (10) ◽  
pp. 1401-1416
Author(s):  
Saugat Aryal ◽  
Dheynoshan Nadarajah ◽  
Prabath Lakmal Rupasinghe ◽  
Chandimal Jayawardena ◽  
Dharshana Kasthurirathna
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Comparative Analysis ◽  
Financial Time Series ◽  
Learning Models ◽  
Financial Time

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.

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