Abstract
Two series, German mark/US dollar exchange rate and US consumer price index time series, are tested to illustrate if noise reduction could help to improve prediction. Three nonlinear noise reduction methods, local projective (LP), singular value decomposition (SVD) and simple nonlinear filtering (SNL), are used to generate the filtered time series. Different projection dimensions of the noise reduction methods are also selected for the sensitivity test on the prediction results. The results show that noise reduction does help in improving prediction in both of the examples providing that an appropriate method of noise reduction and suitable parameter values for the method are used.
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Soofi, A.S., Cao, L. (2002). Nonlinear Forecasting of Noisy Financial Data. 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_22
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DOI: https://doi.org/10.1007/978-1-4615-0931-8_22
Publisher Name: Springer, Boston, MA
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