Abstract
The change of the number of sunspots has a great impact on the Earth's climate, agriculture, communications, natural disasters, and other aspects, so it is very important to predict the number of sunspots. Aiming at the chaotic characteristics of monthly mean of sunspots, a novel hybrid model for forecasting sunspots time-series based on variational mode decomposition (VMD) and backpropagation (BP) neural network improved by firefly algorithm (FA) is proposed. Firstly, a set of intrinsic mode functions (IMFs) are obtained by VMD decomposition of the monthly mean time series of the sunspots. Secondly, the firefly algorithm is introduced to initialize the weights and thresholds of the BP neural network, and a prediction model is established for each IMF. Finally, the predicted values of these components are calculated to obtain the final predict results. Comparing BP model, FA-BP model, EMD-BP model, and VMD-BP model, the simulation results show that the proposed algorithm has higher prediction accuracy and can be used to forecast the time series of sunspots.
Highlights
Sunspot is the most basic and obvious activity in the solar activity, and the sunspot numbers are an indicator of the total solar activity level
In reference [18], the variational mode decomposition (VMD) was used to predict the wind speed sequence and greatly reduced the data complexity and improved the prediction accuracy. erefore, this paper proposes a novel hybrid forecasting model based on VMD and firefly algorithm (FA) to optimize BP neural network (FA-BP) and applies the model to the forecast of sunspot numbers
In this paper, aiming at the problem of forecasting the monthly mean time series of sunspots, a hybrid forecasting model based on variational mode decomposition and re y algorithm to optimize BP neural network is proposed
Summary
Sunspot is the most basic and obvious activity in the solar activity, and the sunspot numbers are an indicator of the total solar activity level. Zhao et al [5] used RBF neural network to predict the smoothed monthly mean sunspot numbers, but the prediction error was gradually enlarged with the prolongation of forecasting time. Hybrid models of EMD, EEMD, and VMD combined with artificial neural networks have been widely used in the field of prediction [9, 10, 14, 15]. E hybrid forecasting models formed by combining VMD with other algorithms have been successfully applied in many fields, such as financial time series [16], stock price evaluation [17], wind speed [18], and so on. Erefore, this paper proposes a novel hybrid forecasting model based on VMD and firefly algorithm (FA) to optimize BP neural network (FA-BP) and applies the model to the forecast of sunspot numbers In reference [18], the VMD was used to predict the wind speed sequence and greatly reduced the data complexity and improved the prediction accuracy. erefore, this paper proposes a novel hybrid forecasting model based on VMD and firefly algorithm (FA) to optimize BP neural network (FA-BP) and applies the model to the forecast of sunspot numbers
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