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:
Shampoo Sales Dataset
Test Setup
Persistence Model Forecast
LSTM Data Preparation
LSTM Model Development
LSTM Forecast
Complete LSTM Example
Develop a Robust Result
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:
链接：
http://machinelearningmastery.com/time-series-forecasting-long-short-term-memory-network-python/
原文链接：
http://weibo.com/1402400261/EDv2dDzSa?from=page_1005051402400261_profile&wvr=6&mod=weibotime&type=comment#_rnd1491644913611
