愉快的学习就从翻译开始吧_Multi-step Time Series Forecasting with Long Short-Term Memory Networks in Python_1

Tutorial Overview/教程概述

This tutorial is broken down into 4 parts; they are:

本教程分为四部分，他们是

1. Shampoo Sales Dataset
   洗发水数据集
2. Data Preparation and Model Evaluation
   数据准备和模型评估
3. Persistence Model
   持续性模型
4. Multi-Step LSTM
   多步LSTM

Environment(不翻译了，和前面几篇一样）

This tutorial assumes you have a Python SciPy environment installed. You can use either Python 2 or 3 with this example.

This tutorial assumes you have Keras v2.0 or higher installed with either the TensorFlow or Theano backend.

This tutorial also assumes you have scikit-learn, Pandas, NumPy, and Matplotlib installed.

If you need help setting up your Python environment, see this post:

• How to Setup a Python Environment for Machine Learning and Deep Learning with Anaconda

Next, let’s take a look at a standard time series forecasting problem that we can use as context for this experiment.