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2020 | OriginalPaper | Chapter

8. Advanced Neural Networks

Authors : Matthew F. Dixon, Igor Halperin, Paul Bilokon

Published in: Machine Learning in Finance

Publisher: Springer International Publishing

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Abstract

This chapter presents various neural network models for financial time series analysis, providing examples of how they relate to well-known techniques in financial econometrics. Recurrent neural networks (RNNs) are presented as non-linear time series models and generalize classical linear time series models such as AR(p). They provide a powerful approach for prediction in financial time series and generalize to non-stationary data. This chapter also presents convolution neural networks for filtering time series data and exploiting different scales in the data. Finally, this chapter demonstrates how autoencoders are used to compress information and generalize principal component analysis.

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Appendix
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Metadata
Title
Advanced Neural Networks
Authors
Matthew F. Dixon
Igor Halperin
Paul Bilokon
Copyright Year
2020
DOI
https://doi.org/10.1007/978-3-030-41068-1_8