Abstract
One of the methods of tackling the problem of predicting the future of financial time series is to look for patterns in the past; these are then used to complete patterns formed by the most recent data, and thus predict future values. For example, the “head and shoulders” pattern is a popular proposed clue from the past. Here we consider more general approaches. We gather together several univariate modelling techniques and show how they lead to models for various assets. We start with the nearest neighbour and GMDH (not described earlier in the book) and then move on to support vector machine and relevance vector machines (described in Chapter 14). We give examples of how these methods can predict various bond markets.
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© 2002 Springer-Verlag London
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Hazarika, N. (2002). Univariate Modelling. In: Shadbolt, J., Taylor, J.G. (eds) Neural Networks and the Financial Markets. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0151-2_23
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DOI: https://doi.org/10.1007/978-1-4471-0151-2_23
Publisher Name: Springer, London
Print ISBN: 978-1-85233-531-1
Online ISBN: 978-1-4471-0151-2
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