问题描述
我有一个时间序列示例数据集.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而导致的差异(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].谁能告诉我这是如何在 python 中完成的
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
假设您的时间序列是一个数组，请尝试以下操作:
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
这篇关于Python - 如何规范化时间序列数据的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！