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Abstract: This paper considers the prediction of noisy time series data, specifically, the prediction of foreign exchange rate data. A novel hybrid neural network algorithm for noisy time series prediction is presented which exhibits excellent performance on the problem. The method is motivated by consideration of how neural networks work, and by fundamental difficulties with random correlations when dealing with small sample sizes and high noise data. The method permits the inference and extraction of... (Update)
Context of citations to this paper: More
.... model (MM) and analyses the correlational structure in the five major XRs through a mixed memory MM [18] Giles, Lawrence and Tsoi [6] considered the same set of five major XRs and predicted the XR directional changes by applying recurrent neural networks to symbolic...
.... models using data from such time series, due to property (1) large samples i.e. time series values over a large time window ([2], 3] are needed. Due to property (2) however, the size of such samples cannot arbitrarily be enlarged. We therefore investigate a...
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BibTeX entry: (Update)
C.L. Giles, S. Lawrence, and A.C. Tsoi. Rule inference for financial prediction using recurrent neural networks. In Proceedings of the conference on Computational Intelligence for Financial Engineering, New York City, NY, pages 253--259, 1997. http://citeseer.ist.psu.edu/giles97rule.html More
@inproceedings{ giles97rule,
author = "C. Lee Giles and Steve Lawrence and A. C. Tsoi",
title = "Rule Inference for Financial Prediction using Recurrent Neural Networks",
booktitle = "Proceedings of {IEEE}/{IAFE} Conference on Computational Intelligence for Financial Engineering ({CIFEr})",
publisher = "IEEE",
address = "Piscataway, NJ",
pages = "253--259",
year = "1997",
url = "citeseer.ist.psu.edu/giles97rule.html" }
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