如何使用scikit-learn for python分析和预测(机器学习)时间序列数据集 [英] how to analyse and predict(machine learning) a time series data set using scikit-learn for python

问题描述

我得到了这样的数据集

我需要分析和预测状态列.这只是训练数据集中的2个主菜.在此数据集中，有一个心率模式(以1秒的间隔收集，总共10个数字)，它是一个时间序列数组(如果我错了，请纠正我)，我只需要知道使用以下方法进行分析并获得预测的最佳方法这个数据.我正在使用scikit-learning进行数据挖掘和机器学习.

i need to analyse and predict the status column. This is just 2 entrees from the training data set. In this data set there is heart rate pattern(which is collected in 1 second intervals, 10 numbers altogether) its a time series array(correct me if i'm wrong) i just need to know best way to analyse and get a prediction using this data. I'm using scikit-learning for my data-mining and machine learning.

我只想知道什么是分析这些时间序列数据的最佳方法?我应该使用基于向量的方法还是其他方法.如果您能给我示例代码，那对我理解该代码将非常有用.

What i just want to know is what is the best way to analyse these time series data? should i use vector based approach or something else. If you can give me example code that would be great for me to understand it.

推荐答案

将心率时间序列中每个点作为一个单独的列，并为所有其他数据点提供一个单独的列(功能).对整个数据集的每一列进行特征归一化(减去均值，除以标准差)，然后输入分类器.

Feed in each point in the heart rate time series as a separate column, along with a separate column (feature) for all of the the other data points. Do feature normalization (substract the mean, divide by the standard deviation) for each column over the entire dataset, and feed into a classifier.

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