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Monthly sunspot number time series analysis and its modeling through autoregressive artificial neural network

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Abstract

This study reports a statistical analysis of monthly sunspot number time series and observes nonhomogeneity and asymmetry within it. Using the Mann-Kendall test a linear trend is revealed. After identifying stationarity within the time series we generate autoregressive AR(p) and autoregressive moving average (ARMA(p, q) . Based on the minimization of AIC we find 3 and 1 as the best values for p and q , respectively. In the next phase, autoregressive neural network (AR-NN(3)) is generated by training a generalized feedforward neural network (GFNN). Assessing the model performances by means of Willmott’s index of second order and the coefficient of determination, the performance of AR-NN(3) is identified to be better than AR(3) and ARMA(3,1).

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Author notes

  1. Surajit Chattopadhyay

    Present address: Visiting Associate of Inter-University Centre for Astronomy and Astrophysics (IUCAA), Pune, India

Authors and Affiliations

  1. Department of Mathematics, Bengal Engineering and Science University, Shibpur, Howrah, 711103, India

    Goutami Chattopadhyay

  2. Pailan College of Management and Technology, Kolkata, 700 104, India

    Surajit Chattopadhyay

Authors
  1. Goutami Chattopadhyay
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  2. Surajit Chattopadhyay
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Correspondence to Surajit Chattopadhyay.

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Chattopadhyay, G., Chattopadhyay, S. Monthly sunspot number time series analysis and its modeling through autoregressive artificial neural network. Eur. Phys. J. Plus 127, 43 (2012). https://doi.org/10.1140/epjp/i2012-12043-9

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  • DOI: https://doi.org/10.1140/epjp/i2012-12043-9

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