The Long Short-Term Memory network or LSTM is a recurrent neural network that can learn and forecast long sequences.

长短期记忆网络或者LSTM是一种可以学习和预测长序列的递归神经网络

A benefit of LSTMs in addition to learning long sequences is that they can learn to make a one-shot multi-step forecast which may be useful for time series forecasting.

除了学习长序列之外，LSTM的好处是他们可以学习做出一步式多步预测（一步输入，输出多步），这对于时间序列预测也许是有用的

A difficulty with LSTMs is that they can be tricky to configure and it can require a lot of preparation to get the data in the right format for learning.

LSTM的困难在于它们配置起来可能很棘手，并且需要大量的准备才能以正确的格式获取数据以进行学习

In this tutorial, you will discover how you can develop an LSTM for multi-step time series forecasting in Python with Keras.

在本教程中，您将了解如何使用Keras在Python中开发用于多步时间序列预测的LSTM。

After completing this tutorial, you will know:

完成本教程后，您将知道：

• How to prepare data for multi-step time series forecasting.
  如何为多步时间序列预测准备数据
• How to develop an LSTM model for multi-step time series forecasting.
  如何开发一个多步时间序列预测的LSTM模型
• How to evaluate a multi-step time series forecast.
  如何评估一个多步时间序列预测

Let’s get started.

让我们开始吧

Multi-step Time Series Forecasting with Long Short-Term Memory Networks in Python
Photo by Tom Babich, some rights reserved.