A Hybrid System Integrating a Wavelet and TSK Fuzzy Rules for Stock Price Forecasting

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Financial market prediction system with Evolino neural network and Delphi method (2013, background): “Chiang and Liu (2008) developed a fuzzy rule based system, where the clustering technique and the simplified and wavelet (Chang, Fan 2008) fuzzy rule based systems were integrated for forecasting.”
Restricted Boltzmann machine based stock market trend prediction (2017, methodology): “To handle the noise hidden in stock data, some researchers smooth raw data by using wavelet transform [29], [30], [31], or separate noisy data to low-volatility and highvolatility by using a moving average filter [32].”
Multiple-output support vector regression with a firefly algorithm for interval-valued stock price index forecasting (2014, methodology): “According to an extensive literature investigation, it is not difficult to find a wide variety of methodologies and techniques that have been used for stock price forecasting with various degrees of success, such as the Box–Jenkins method [1], general autoregressive conditional heteroskedasticity [2”
Wavelet low- and high-frequency components as features for predicting stock prices with backpropagation neural networks (2014, methodology): “Moreover, the popular Daubechies wavelet is a compactly supported orthonormal wavelet and provides accurate time series predictions; hence, it is widely used in financial time series forecasting problems (Chang and Fan, 2008; Huang, 2011; Huang and Wu, 2010).”