Time series data from real problems have nonlinear, non-smooth, and multi-scale composite characteristics. This paper first proposes a gated recurrent unit-correction (GRU-corr) network model, which adds a correction layer to the GRU neural network. Then, a adaptive staged variation PSO (ASPSO) is proposed. Finally, to overcome the drawbacks of the imprecise selection of the GRU-corr network parameters and obtain the high-precision global optimization of network parameters, weight parameters and the hidden nodes number of GRU-corr is optimized by ASPSO, and a time series prediction model (ASPSO-GRU-corr) is proposed based on the GRU-corr optimized by ASPSO. In the experiment, a comparative analysis of the optimization performance of ASPSO on a benchmark function was performed to verify its validity, and then the ASPSO-GRU-corr model is used to predict the ship motion cross-sway angle data. The results show that, ASPSO has better optimization performance and convergence speed compared with other algorithms, while the ASPSO-GRU-corr has higher generalization performance and lower architecture complexity. The ASPSO-GRU-corr can reveal the intrinsic multi-scale composite features of the time series, which is a reliable nonlinear and non-steady time series prediction method.

中文翻译：

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改进的 PSO 算法优化的 GRU-corr 神经网络用于时间序列预测

来自实际问题的时间序列数据具有非线性、非光滑和多尺度复合特征。本文首先提出了门控循环单元校正（GRU-corr）网络模型，在GRU神经网络中增加了一个校正层。然后，提出了一种自适应分级变化粒子群算法（ASPSO）。最后，为了克服GRU-corr网络参数选择不精确的弊端，获得网络参数的高精度全局优化，通过ASPSO对GRU-corr的权重参数和隐藏节点数进行优化，并进行时间序列预测模型（ASPSO-GRU-corr）是基于 ASPSO 优化的 GRU-corr 提出的。在实验中，对 ASPSO 在基准函数上的优化性能进行了对比分析，以验证其有效性，然后使用ASPSO-GRU-corr模型预测船舶运动横摆角数据。结果表明，与其他算法相比，ASPSO具有更好的优化性能和收敛速度，而ASPSO-GRU-corr具有更高的泛化性能和更低的架构复杂度。ASPSO-GRU-corr可以揭示时间序列内在的多尺度复合特征，是一种可靠的非线性非稳态时间序列预测方法。