Python - 如何标准化时间序列数据

Python - how to normalize time-series data(Python - 如何标准化时间序列数据)

问题描述

我有一个时间序列示例的数据集.我想计算各种时间序列示例之间的相似性，但是我不想考虑由于缩放引起的差异(即我想查看时间序列形状的相似性，而不是它们的绝对值).因此，为此，我需要一种标准化数据的方法.也就是说，使所有时间序列示例都落在某个区域之间，例如 [0,100].谁能告诉我如何在 python 中做到这一点

I have a dataset of time-series examples. I want to calculate the similarity between various time-series examples, however I do not want to take into account differences due to scaling (i.e. I want to look at similarities in the shape of the time-series, not their absolute value). So, to this end, I need a way of normalizing the data. That is, making all of the time-series examples fall between a certain region e.g [0,100]. Can anyone tell me how this can be done in python

推荐答案

假设你的时间序列是一个数组，试试这样:

Assuming that your timeseries is an array, try something like this:

(timeseries-timeseries.min())/(timeseries.max()-timeseries.min())

这会将您的值限制在 0 和 1 之间

This will confine your values between 0 and 1

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