张量是一个序列_张量流中使用gru和bilstm进行预测分析的时间序列预测

张量是一个序列

Recurrent Neural Networks are designed to handle the complexity of sequence dependence in time-series analysis. In this tutorial, I build GRU and BiLSTM for a univariate time-series predictive model. Gated Recurrent Unit (GRU) is a new generation of Neural Networks and is pretty similar to Long Short Term Memory (LSTM). Whereas, the idea of Bidirectional LSTMs (BiLSTM) is to aggregate input information in the past and future of a specific time step in LSTM models.

řecurrent神经网络被设计为处理中的时间序列分析序列依赖性的复杂性。 在本教程中，我将为单变量时间序列预测模型构建GRU和BiLSTM。 门控循环单元(GRU)是新一代的神经网络，与长期短期记忆(LSTM)非常相似。 双向LSTM(BiLSTM)的思想是在LSTM模型中特定时间步的过去和将来汇总输入信息。

The following article serves a good introduction to LSTM, GRU and BiLSTM.

以下文章很好地介绍了LSTM，GRU和BiLSTM。

什么是时间序列分析？ (What is the time-series analysis