【推荐】(Python)LSTM时序预测

The Long Short-Term Memory recurrent neural network has the promise of learning long sequences of observations.

It seems a perfect match for time series forecasting, and in fact, it may be.

In this tutorial, you will discover how to develop an LSTM forecast model for a one-step univariate time series forecasting problem.

After completing this tutorial, you will know:

• How to develop a baseline of performance for a forecast problem.

• How to design a robust test harness for one-step time series forecasting.

• How to prepare data, develop, and evaluate an LSTM recurrent neural network for time series forecasting.

Let’s get started.

Tutorial Overview

This is a big topic and we are going to cover a lot of ground. Strap in.

This tutorial is broken down into 9 parts; they are:

1. Shampoo Sales Dataset

2. Test Setup

3. Persistence Model Forecast

4. LSTM Data Preparation

5. LSTM Model Development

6. LSTM Forecast

7. Complete LSTM Example

8. Develop a Robust Result

9. Tutorial Extensions

Python Environment

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

You must have Keras (2.0 or higher) installed with either the TensorFlow or Theano backend.

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

If you need help with your environment, see this post:

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