scholarly journals Modeling financial time-series with generative adversarial networks

2019 ◽  
Vol 527 ◽  
pp. 121261 ◽  
Author(s):  
Shuntaro Takahashi ◽  
Yu Chen ◽  
Kumiko Tanaka-Ishii
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Generative Adversarial Networks ◽  
Financial Time ◽  
Adversarial Networks

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.

scholarly journals Detecting Predictable Segments of Chaotic Financial Time Series via Neural Network

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 823
Author(s):  
Tianle Zhou ◽  
Chaoyi Chu ◽  
Chaobin Xu ◽  
Weihao Liu ◽  
Hao Yu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Phase Space ◽  
Financial Market ◽  
Financial Time Series ◽  
Influential Factors ◽  
Series Data ◽  
Phase Point ◽  
Price Fluctuations ◽  
Financial Time

In this study, a new idea is proposed to analyze the financial market and detect price fluctuations, by integrating the technology of PSR (phase space reconstruction) and SOM (self organizing maps) neural network algorithms. The prediction of price and index in the financial market has always been a challenging and significant subject in time-series studies, and the prediction accuracy or the sensitivity of timely warning price fluctuations plays an important role in improving returns and avoiding risks for investors. However, it is the high volatility and chaotic dynamics of financial time series that constitute the most significantly influential factors affecting the prediction effect. As a solution, the time series is first projected into a phase space by PSR, and the phase tracks are then sliced into several parts. SOM neural network is used to cluster the phase track parts and extract the linear components in each embedded dimension. After that, LSTM (long short-term memory) is used to test the results of clustering. When there are multiple linear components in the m-dimension phase point, the superposition of these linear components still remains the linear property, and they exhibit order and periodicity in phase space, thereby providing a possibility for time series prediction. In this study, the Dow Jones index, Nikkei index, China growth enterprise market index and Chinese gold price are tested to determine the validity of the model. To summarize, the model has proven itself able to mark the unpredictable time series area and evaluate the unpredictable risk by using 1-dimension time series data.

scholarly journals Selecting between causal and noncausal models with quantile autoregressions

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Alain Hecq ◽  
Li Sun
Keyword(s):  
Time Series ◽  
Maximum Likelihood ◽  
Latin American ◽  
Financial Time Series ◽  
Selection Criterion ◽  
Likelihood Methods ◽  
Approximate Maximum Likelihood ◽  
Financial Time ◽  
Maximum Likelihood Methods ◽  
Latin American Countries

AbstractWe propose a model selection criterion to detect purely causal from purely noncausal models in the framework of quantile autoregressions (QAR). We also present asymptotics for the i.i.d. case with regularly varying distributed innovations in QAR. This new modelling perspective is appealing for investigating the presence of bubbles in economic and financial time series, and is an alternative to approximate maximum likelihood methods. We illustrate our analysis using hyperinflation episodes of Latin American countries.

Sign in / Sign up

Export Citation Format

Share Document