Regularised gradient boosting for financial time-series modelling

2017 ◽  
Vol 14 (3) ◽  
pp. 367-391 ◽  
Author(s):  
Alexandros Agapitos ◽  
Anthony Brabazon ◽  
Michael O’Neill
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Gradient Boosting ◽  
Financial Time ◽  
Time Series Modelling

scholarly journals Financial time series modelling: return on assets

2019 ◽  
Vol 5 (2(49)) ◽  
pp. 50-55
Author(s):  
Mykola Kushnir ◽  
Kateryna Tokarieva
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Return On Assets ◽  
Financial Time ◽  
Time Series Modelling

scholarly journals Financial Time Series Prediction Based on XGBoost and Generative Adversarial Networks

2022 ◽  
Vol 16 ◽  
pp. 637-645
Author(s):  
Jialing Xu ◽  
Jingxing He ◽  
Jinqiang Gu ◽  
Huayang Wu ◽  
Lei Wang ◽  
...  
Keyword(s):  
Time Series ◽  
Prediction Accuracy ◽  
Financial Time Series ◽  
Time Series Prediction ◽  
Generative Adversarial Networks ◽  
Gradient Boosting ◽  
Financial Time ◽  
Adversarial Networks ◽  
Extreme Gradient Boosting ◽  
Gp Model

Considering the problems of the model collapse and the low forecast precision in predicting the financial time series of the generative adversarial networks (GAN), we apply the WGAN-GP model to solve the gradient collapse. Extreme gradient boosting (XGBoost) is used for feature extraction to improve prediction accuracy. Alibaba stock is taken as the research object, using XGBoost to optimize its characteristic factors, and training the optimized characteristic variables with WGAN-GP. We compare the prediction results of WGAN-GP model and classical time series prediction models, long short term memory (LSTM) and gate recurrent unit (GRU). In the experimental stage, root mean square error (RMSE) is chosen as the evaluation index. The results of different models show that the RMSE of WGAN-GP model is the smallest, which are 61.94% and 47.42%, lower than that of LSTM model and GRU model respectively. At the same time, the stock price data of Google and Amazon confirm the stability of WGAN-GP model. WGAN-GP model can obtain higher prediction accuracy than the classical time series prediction model.

Financial time series modelling with discounted least squares backpropagation

1997 ◽  
Vol 14 (2) ◽  
pp. 123-138 ◽  
Author(s):  
A.N. Refenes ◽  
Y. Bentz ◽  
D.W. Bunn ◽  
A.N. Burgess ◽  
A.D. Zapranis
Keyword(s):  
Time Series ◽  
Least Squares ◽  
Financial Time Series ◽  
Financial Time ◽  
Time Series Modelling
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