Forecasting via Wavelet Denoising: The Random Signal Case

Joanna Bruzda

A chapter in Wavelet Applications in Economics and Finance, 2014, pp 187-225 from Springer

Abstract: Abstract In the paper we evaluate the usability of certain wavelet-based methods of signal estimation for forecasting economic time series. We concentrate on extracting stochastic signals embedded in white noise with the help of wavelet scaling based on the non-decimated version of the discrete wavelet transform. The methods used here can be thought of as a type of smoothing, with weights depending on the frequency content of the examined processes. Both our simulation study and empirical examination based on time series from the M3-JIF Competition database show that the suggested forecasting procedures may be useful in economic applications.

Keywords: Wavelet Denoising; Forecasting Procedures; Signal Estimator; Maximal Overlap Discrete Wavelet Transform (MODWT); Forecast MSEs (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:spr:dymchp:978-3-319-07061-2_9

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DOI: 10.1007/978-3-319-07061-2_9

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