数据处理对时序预测是很重要的，这篇文章很有帮助
How to Convert a Time Series to a Supervised Learning Problem in Python/在Python中如何把时序转换为监督学习问题
Machine learning methods like deep learning can be used for time series forecasting.
像深度学习这样的机器学习方法可以用于时序预测
Before machine learning can be used, time series forecasting problems must be re-framed as supervised learning problems. From a sequence to pairs of input and output sequences.
在使用机器学习之前，时序预测问题必须被重新构建为监督学习问题，从一个序列到输入和输出对的序列。
In this tutorial, you will discover how to transform univariate and multivariate time series forecasting problems into supervised learning problems for use with machine learning algorithms.
在本教程中，您将了解如何将单变量和多变量时间序列预测问题转换为与机器学习算法一起使用的监督学习问题。
After completing this tutorial, you will know:
完成本教程后你将知道：
- How to develop a function to transform a time series dataset into a supervised learning dataset.
如何开发一个函数来转换一个时序数据集到一个监督学习数据集。 - How to transform univariate time series data for machine learning.
如何为机器学习转化单变量时序数据 - How to transform multivariate time series data for machine learning.
如何为机器学习转化多变量时序数据
Let’s get started./让我们开始吧。
How to Convert a Time Series to a Supervised Learning Problem in Python
Photo by Quim Gil, some rights reserved.
Time Series vs Supervised Learning/时间序列与监督学习
Before we get started, let’s take a moment to better understand the form of time series and supervised learning data.
在我们开始之前，让我们花点时间来更好的理解时间序列和监督学习数据的格式。
A time series is a sequence of numbers that are ordered by a time index. This can be thought of as a list or column of ordered values.
时间序列是按时间索引排序的一个数字序列。 这可以被认为是有序值的列表或列。
For example:
例如：
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1
2
3
4
5
6
7
8
9
10
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0
1
2
3
4
5
6
7
8
9
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A supervised learning problem is comprised of input patterns (X) and output patterns (y), such that an algorithm can learn how to predict the output patterns from the input patterns.
监督学习问题由输入模式（X）和输出模式（y），这样算法能够学习如何根据输入模式预测输出模式。
For example:
例如：
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1
2
3
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5
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7
8
9
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X, y
1 2
2, 3
3, 4
4, 5
5, 6
6, 7
7, 8
8, 9
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