https://doi.org/10.1140/epjp/i2012-12043-9
Regular Article
Monthly sunspot number time series analysis and its modeling through autoregressive artificial neural network
1
Department of Mathematics, Bengal Engineering and Science University, Shibpur, Howrah, 711103, India
2
Pailan College of Management and Technology, Kolkata, 700 104, India
* e-mail: surajit_2008@yahoo.co.in
** e-mail: surajcha@iucaa.ernet.in
Received:
13
January
2012
Revised:
9
March
2012
Accepted:
27
March
2012
Published online:
20
April
2012
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).
© Società Italiana di Fisica and Springer-Verlag Berlin Heidelberg, 2012