In real-world complex multivariate time series data, mutation phenomena can significantly affect variation rules of target series. Meanwhile, there is no specific learning mechanism for the current deep learning model to capture mutation information in time series prediction. To this end, we propose a new deep learning model to capture mutation information between data. To capture the impact of mutation information on target series, a new function mapping is designed in the attention mechanism of the encoder to process the fusion of historical hidden state and cell state information; and an LSTM with transformation mechanism is proposed in the encoder to process the input information flow and learn the mutation information. In addition, an adaptive self-paced curriculum learning mechanism is designed to obtain mutation information that may be ignored among mini-batch samples. Finally, we define an objective function for multivariate time series prediction, which can extract the influence of temporal correlation information and mutation information within the data on target series. Our model can achieve superior performance than all baseline methods on five real-world datasets in different fields.

在现实世界中复杂的多元时间序列数据中，突变现象会显着影响目标序列的变异规则。同时，当前的深度学习模型没有特定的学习机制来捕获时间序列预测中的突变信息。为此，我们提出了一种新的深度学习模型来捕获数据之间的突变信息。为了捕获突变信息对目标序列的影响，在编码器的注意力机制中设计了一个新的函数映射，以处理历史隐藏状态和单元状态信息的融合。在编码器中提出了一种具有变换机制的LSTM，用于处理输入信息流并学习突变信息。此外，设计了一种自适应的自定进度课程学习机制，以获取小批量样本中可能忽略的突变信息。最后，我们定义了用于多元时间序列预测的目标函数，该函数可以提取数据中时间相关信息和变异信息对目标序列的影响。与在不同领域中的五个真实数据集上的所有基线方法相比，我们的模型可实现比所有基线方法更高的性能。