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Computational Intelligence Techniques in Earth and Environmental Sciences pp 141–154Cite as

Performance of Wavelet Transform on Models in Forecasting Climatic Variables

Abstract

An attempt has been made to show whether the recently developed wavelet transformation in forecasting the climatic time series in Bangladesh improves the performance of existing forecasting models, such as ARIMA. These models are applied to forecast the humidity of Rajshahi, Bangladesh. Then the wavelet transformation has been used to decompose the humidity series into a set of better-behaved constitutive series. These decomposed series and inverse wavelet transformation are used as a pre-processing procedure of forecasting humidity series using the same models in two approaches. Finally, the forecasting ability of these two models with and without wavelet transformation is compared using the statistical forecasting accuracy criteria. The results show that the use of wavelet transformation as a pre-processing procedure of forecasting climatic time series improves the performance of forecasting models. The reason is the better behavior of the constitutive series for the filtering effect of the wavelet transform.

Keywords

  • Wavelet transformation
  • ARIMA models
  • Forecasting

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Authors and Affiliations

  1. Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh

    Md. Jahanur Rahman

  2. Department of Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh

    Md. Al Mehedi Hasan

Authors
  1. Md. Jahanur Rahman
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  2. Md. Al Mehedi Hasan
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Corresponding author

Correspondence to Md. Jahanur Rahman .

Editor information

Editors and Affiliations

  1. Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO, USA, NOAA/NESDIS Center for Satellite Applications and Research, College Park, MD, USA, Fort Collins, CO, USA

    Tanvir Islam

  2. NASA Goddard Space Flight Center, Greenbelt & ESSIC, University of Maryland College Park, MD, USA, Greenbelt, Maryland, USA

    Prashant K. Srivastava

  3. Department of Civil Engineering, IIT Delhi, Delhi, India

    Manika Gupta

  4. School of Geog. and Env. Sciences, Monash University, Melbourne, Australia

    Xuan Zhu

  5. School of Environmental Sciences Remote Sensing, Jawaharlal Nehru University, New Delhi, India

    Saumitra Mukherjee

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© 2014 Springer Science+Business Media Dordrecht

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Rahman, M.J., Hasan, M.A.M. (2014). Performance of Wavelet Transform on Models in Forecasting Climatic Variables. In: Islam, T., Srivastava, P., Gupta, M., Zhu, X., Mukherjee, S. (eds) Computational Intelligence Techniques in Earth and Environmental Sciences. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-8642-3_8

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