关键词: 改进果蝇优化算法, 直连长短期记忆网络, 时序预测, 预测精度

Abstract:

In order to improve the prediction accuracy of time series, a time series prediction method based on improved fruit fly algorithm to optimize connect inputs and outputs in long short-term memory network is proposed. Firstly, the input and output of time sets in the memory system is fully connected（Connect Inputs and Outputs in Long Short-Term Memory, CIAO-LSTM）, thus, the characterization of linear components in the target system is enhanced. Secondly, an Improved Fruit fly Optimization Algorithm（IFOA） is proposed. By dynamically changing the search radius of Drosophila and increasing the escape coefficient to the fitness function, the global optimization ability and local convergence speed of Drosophila optimization algorithm are improved. Finally, IFOA is used to optimize the CIAO-LSTM network parameters and build a predictive model（IFOA_CIAO-LSTM）. The experimental results show that the optimized time series prediction method is more generalized and more accurate than traditional long-and short-term memory networks, and can be better fitted to the data with large fluctuations.

Key words: improved fruit fly optimization algorithm, connect inputs and outputs in long short-term memory network, time series prediction, prediction accuracy