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Using wavelets for time series forecasting: Does it pay off?

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  • Schlüter, Stephan
  • Deuschle, Carola
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    Abstract

    By means of wavelet transform a time series can be decomposed into a time dependent sum of frequency components. As a result we are able to capture seasonalities with time-varying period and intensity, which nourishes the belief that incorporating the wavelet transform in existing forecasting methods can improve their quality. The article aims to verify this by comparing the power of classical and wavelet based techniques on the basis of four time series, each of them having individual characteristics. We find that wavelets do improve the forecasting quality. Depending on the data's characteristics and on the forecasting horizon we either favour a denoising step plus an ARIMA forecast or an multiscale wavelet decomposition plus an ARIMA forecast for each of the frequency components. --

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    Bibliographic Info

    Paper provided by Friedrich-Alexander-Universität Erlangen-Nürnberg, Institut für Wirtschaftspolitik und Quantitative Wirtschaftsforschung (IWQW) in its series IWQW Discussion Paper Series with number 04/2010.

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    Date of creation: 2010
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    Handle: RePEc:zbw:iwqwdp:042010

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    Web page: http://www.iwqw.rw.uni-erlangen.de/
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    Related research

    Keywords: Forecasting; Wavelets; ARIMA; Denoising; Multiscale Analysis;

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    Cited by:
    1. Schnitzlein, Daniel D., 2012. "How important is cultural background for the level of intergenerational mobility?," Economics Letters, Elsevier, vol. 114(3), pages 335-337.
    2. Tinkl, Fabian, 2010. "A note on Hadamard differentiability and differentiability in quadratic mean," IWQW Discussion Paper Series 08/2010, Friedrich-Alexander-Universität Erlangen-Nürnberg, Institut für Wirtschaftspolitik und Quantitative Wirtschaftsforschung (IWQW).
    3. Nowotarski, Jakub & Tomczyk, Jakub & Weron, Rafal, 2012. "Robust estimation and forecasting of the long-term seasonal component of electricity spot prices," MPRA Paper 42563, University Library of Munich, Germany.
    4. Kriechbaumer, Thomas & Angus, Andrew & Parsons, David & Rivas Casado, Monica, 2014. "An improved wavelet–ARIMA approach for forecasting metal prices," Resources Policy, Elsevier, vol. 39(C), pages 32-41.

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