测试多列的列车数据帧

Question

我有一个csv文件

Date,Open,High,Low,Close,Adj Close,Volume,Cash EPS,Book Value,Div/share,Net profit/share,NPM,ROE,ROCE,ROA,DEBT/EQ,ATR,CR
2004-04-26,82.924217,82.924217,82.924217,82.924217,60.026066,0,221.24,488.21,129.5,186.6,26.11,38.22,38.22,24.2,0,92.67,1.65
2004-04-27,82.778122,82.778122,79.765625,80.24453,58.086323,28616000,221.24,488.21,129.5,186.6,26.11,38.22,38.22,24.2,0,92.67,1.65

为了便于计算，只给出了2行。我已经创建了一个数据帧

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from sklearn.preprocessing import MinMaxScaler

dataframe1 = pd.read_csv('test.csv')
df = dataframe1.dropna()
scaler=MinMaxScaler(feature_range=(0,1))
df1=scaler.fit_transform(np.array(df1).reshape(-1,1))
min_max_scaler = MinMaxScaler()
df[["Open", "High", "Low", "Close", "Adj Close", "Volume", "Book Value", "Div/share", "Net profit/share", "NPM", "ROE", "ROCE", "ROA", "DEBT/EQ", "ATR", "CR"]] = min_max_scaler.fit_transform(df[["Open", "High", "Low", "Close", "Adj Close", "Volume", "Book Value", "Div/share", "Net profit/share", "NPM", "ROE", "ROCE", "ROA", "DEBT/EQ", "ATR", "CR"]])

为了训练数据集，我需要日期和预测，例如关闭列。但是，关闭列值取决于多个列(即此csv中存在的所有列)

我如何训练date和close列的数据，但基于所有其他列，以便可以预测将来的close？

Answer 1

如果我理解这个问题，你正在寻找一个多变量的时间序列模型。换句话说，它为每个时间步接受多个变量输入，以便做出前瞻性预测。以下是一些示例的链接：

https://www.relataly.com/stock-market-prediction-with-multivariate-time-series-in-python/1815/

另外，我建议你去看看Kaggle股市预测大赛，有成百上千的例子说明人们是如何处理这个问题的。

https://www.kaggle.com/c/two-sigma-financial-news