Forecasting stock markets using wavelet transforms and recurrent neural networks: An integrated system based on artificial bee colony algorithm

By Tsung-Jung Hsieh, Hsiao-Fen Hsiao, Wei-Chang Yeh. 2011

411 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Economics and Econometrics, Management Science and Operations Research, Model Architectures, Time-Series & Forecasting

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Citations in the corpus

Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (2019, methodology): “The authors of [123, 124, 135, 136, 137] used NIKKEI as their dataset.”
A Feature Fusion Based Forecasting Model for Financial Time Series (2014, methodology): “Subsequently, Back Propagation Neural Network [12], Procedural Neural Networks [13], Probabilistic Neural Network[14], Functional Link Artificial Neural Network [15], Recurrent Neural Network [16], and Radial Basis Function Neural Network (RBFNN) [17] have been proposed for application to stock mark”
Forecasting East Asian Indices Futures via a Novel Hybrid of Wavelet-PCA Denoising and Artificial Neural Network Models (2016, methodology): “[28] propose an ensemble system in which wavelet denoising and a recurrent neural network are combined to forecast DJIA, FTSE, NIKKEI, and TAIEX with promising profitable trading results.”
Combining the real-time wavelet denoising and long-short-term-memory neural network for predicting stock indexes (2017, background): “Furthermore, a few studies have attempted to apply wavelet denoising combined with other artificial neural network architectures to the stock market prediction according to [5, 11, 12].”
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].”
Financial Time Series Prediction Based on Deep Learning (2017, background): “Recurrent neural network (RNN) can achieve better predictive results in the prediction of stock prices [11].”
Multivariate Time Series Imputation with Generative Adversarial Networks (2018, background): “Time series analysis is useful in many situations such as forecasting the stock price [22] and indicating fitness and diagnosis category of patients [7].”
SAITS: Self-Attention-based Imputation for Time Series (2022, background): “Multivariate time-series data is ubiquitous in many application domains, for instance, transportation [1, 2], economics [3, 4], healthcare [5, 6, 7], and meteorology [8, 9, 10].”
Application of type-2 neuro-fuzzy modeling in stock price prediction (2012, methodology): “The given training data set is partitioned into clusters through input-similarity and output-similarity tests, and a type-2 TSK rule is derived from each cluster to form a fuzzy rule base.”
Position-Based Content Attention for Time Series Forecasting with Sequence-to-Sequence RNNs (2017, methodology): “Subse-quently, various RNN-based models were developed for different time series, as noisy foreign exchange rate prediction [18], chaotic time series prediction in communication engineering [19] or stock price prediction [20].”
Wavelet low- and high-frequency components as features for predicting stock prices with backpropagation neural networks (2014, methodology): “Hsieh et al. (2011) applied the wavelet decomposition to analyze the stock price time series of the Dow Jones Industrial Average Index (DJIA), London FTSE-100 Index (FTSE), Tokyo Nikkei-225 Index (Nikkei) and Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX).”