Noisy Time Series Prediction using Recurrent Neural Networks and Grammatical Inference

By C. Lee Giles, Steve Lawrence, Ah Chung Tsoi. 2001

414 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research

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Citations in the corpus

Rule Extraction from Recurrent Neural Networks: ATaxonomy and Review (2005, methodology): “& Omlin 1994, Goudreau & Giles 1995, Giles et al. 1997, Lawrence et al. 1998, Lawrence, Giles & Fong 2000, Giles et al. 2001, Bakker 2004).”
Empirical evaluation of an automated intraday stock recommendation system incorporating both market data and textual news (2014, background): “Most studies that predict future stock returns rely primarily on historical market data such as stock price and trading volume (e.g., [25,33,38,56]).”
Bibliography of Self-Organizing Map SOM) Papers: 1998-2001 Addendum (2003, neutral): “software [199, 283, 336, 338, 391, 423, 636, 455, 520, 557, 560, 581, 834, 709, 805, 904, 905, 1110, 1253, 1285, 1355, 1378, 1384, 1449, 1464, 1500, 1509, 1530, 1668, 1672, 1722, 1732, 1795, 1794, 2087, 1833, 1874, 1897, 2032, 2033, 2031] solder joint(s) [840, 1499, 2051] sonar [14, 245, 739, 860, 1”
Comparative study on methods of detecting research fronts using different types of citation (2009, background): “Neural networks are good tools for identifying relationships between inputs and outputs from a set of examples; therefore, neural networks are goodcandidatesfortransactionloganalysis.Duetotheneural networks approximation properties as well as their inherent adaptation features, neural networks have ”
A Machine Learning Method for Extracting Symbolic Knowledge from Recurrent Neural Networks (2004, methodology): “It has been successfully used in an exchange rate prediction application based on that in Giles, Lawrence, and Tsoi (2001) and in seismic event modeling for data reported in Van Zyl and Omlin (2001).”
Predicting the Price of Bitcoin Using Machine Learning (2018, background): “[20] notes that the temporal relationship of the series is explicitly modelled by the internal states contributing significantly to model effectiveness.”
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].”
Deep learning with long short-term memory networks for financial market predictions (2017, background): “…volume-weighted-average-prices the results Selected applications, as in Xiong et al. (2015), focus on predicting the volatility of the S&P 500, on selected foreign exchange rates (Giles et al., 2001), or on the impact of incorporating news for selected companies (Siah and Myers, n.d.).”