Abstract
A global approximation technique, wavelet network, is introduced in this chapter to predict nonlinear time series. Applications of this technique are demonstrated by testing predictions, in particular, short-term predictions, on various artificial time series generated from chaotic systems as well as on real world economic time series. Prediction tests are also conducted on time series from a dynamical system with its parameter varying over the time (either the parameter corrupted by noise over the time or the parameter varying following a certain rule): this may often be the case in economic systems where the parameters of the systems are unlikely to remain constant over the time. Numerical results in both artificial and real time series showed the capability of the technique.
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Cao, L. (2002). Nonlinear Prediction of Time Series Using Wavelet Network Method. In: Soofi, A.S., Cao, L. (eds) Modelling and Forecasting Financial Data. Studies in Computational Finance, vol 2. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-0931-8_9
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DOI: https://doi.org/10.1007/978-1-4615-0931-8_9
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