股票预测线性回归 [英] Stock Prediction Linear Regression

问题描述

我们正在使用Azure机器学习工作室获取具有日期，开盘价，最高价，最低价，调整后收盘价和成交量数据的股票市场数据，并使用预测调整后的收盘价线性回归。

We are using Azure Machine learning studio for stock market data with Date,Open,High,Low, Adjusted close and Volume variables and predicting adjusted close price using Linear regression.

我们在分裂数据中加0.8，在随机种子中加0。我们得到了非常高的预测和几乎100％的确定系数。可以解释一下我们在做什么错误！

We put 0.8 in split data and 0 in random seed.We are getting a very high prediction and coefficient of determination of almost 100%.Can someone explain what are we probably doing wrong!

推荐答案

 

Hi, 

也许，其他用户可能会有一些额外的解释。我怀疑有一些独立变量与您的因变量密切相关。 

Perhaps, other users might have some additional explanation. I suspect there is some independent variable that is strongly correlated to your dependent variable. 

以下是一些关于图库页面上股票预测的示例实验：

Here are some sample experiments on stock prediction on the gallery page:

https://gallery.azure.ai/browse?s=stock%20prediction&skip=0&examples=false

此致，

Jaya

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