Further Reading/进一步阅读
This section provides more resources on the topic if you are looking go deeper.
如果您想更深入研究，本节将提供更多有关该主题的资源。
- Beijing PM2.5 Data Set on the UCI Machine Learning Repository
- The 5 Step Life-Cycle for Long Short-Term Memory Models in Keras
- Time Series Forecasting with the Long Short-Term Memory Network in Python
- Multi-step Time Series Forecasting with Long Short-Term Memory Networks in Python
Summary/总结
In this tutorial, you discovered how to fit an LSTM to a multivariate time series forecasting problem.
在本教程中，您了解了如何将LSTM应用于多变量时间序列预测问题。
Specifically, you learned:
具体来说，你了解到：
- How to transform a raw dataset into something we can use for time series forecasting.
如何将原始数据集转换为我们可用于时间序列预测的东西。 - How to prepare data and fit an LSTM for a multivariate time series forecasting problem.
如何准备数据并拟合多变量时间序列预测问题的LSTM。 - How to make a forecast and rescale the result back into the original units.
如何进行预测并将结果重新调整回原始单位。
Do you have any questions?
Ask your questions in the comments below and I will do my best to answer.
你有任何问题吗？
在下面的评论中提出您的问题，我会尽我所能来回答。