Stock market trend prediction using ARIMA-based neural networks

By Jung-Hua Wang, Jia-Yann Leu. 1996

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Financial market prediction system with Evolino neural network and Delphi method (2013, methodology): “Kimoto et al. (1990) proposed a stock market prediction system with modular neural networks; Wang and Leu (1996) used ARIMA-based neural networks.”
Combining neural network model with seasonal time series ARIMA model (2002, methodology): “Wang and Leu [12] used a hybrid model to forecast the mid-term price trend of the Taiwan stock exchange weighted stock index.”
Neural Networks Fail to Learn Periodic Functions and How to Fix It (2020, methodology): “Qual-itatively, we focus on making comparison with ARIMA, a traditional and standard method in economics and stock price prediction [26, 2, 37].”
Pattern graph tracking-based stock price prediction using big data (2018, background): “…the new pattern data belongs to a certain pattern category, artiﬁcial neural network (ANN) ([31, 32]), to have good prediction even with a complex relationship between the variables, and autoregressive integrated moving average (ARIMA) ([40, 46]) to identify and predict time series variation.”
Discovering golden nuggets: data mining in financial application (2004, methodology): “For example, Wang and Leu [ 9 ] developed a recurrent BP neural network for forecasting mid-term price trend of Taiwan stock market.”