使用 k 最近邻回归时拟合错误

Question

我使用最近邻法来预测股票的价格。 我在 example.txt 文件中有原始数据。 我使用收盘列（周期结束时的价格 = 1 分钟）。 线性回归预测良好（以绿色显示）。 但是最近邻居的方法只在开始时有效，然后变成一条直线，请告诉我如何解决这个问题？ 这是我写的代码：

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.neighbors import KNeighborsRegressor


class Reader:

def __init__(self, filename='example.txt'):
    self.filename = filename

def read(self):
    try:
        file = open(self.filename)
        return file.read()
    except IOError:
        return "File not found"

def main():
    x = Reader('example.txt')
    print(x.read())


class Regression:

def __init__(self, window, P0, Ptest, i):
    self.window = window
    self.P0 = P0
    self.Ptest = Ptest
    self.i = i
    self.data_train = self.get_data_train()
    self.x_train = self.get_x_train()
    self.y_train = self.get_y_train()
    self.data_test = self.get_data_test()
    self.x_test = self.get_x_test()
    self.y_test = self.get_y_test()

def get_data_train(self):
    """ Method of obtaining data train on prices for the entire period."""
    x = Reader('example.txt')
    data = x.read().splitlines()
    close_column = [x.split(',')[7] for x in data][1:]
    result = [float(item) for item in close_column]
    relative_price = result[:int(len(result)*P0)]
    return relative_price

def get_data_test(self):
    """ Method of obtaining data test on prices for the entire period."""
    x = Reader('example.txt')
    data = x.read().splitlines()
    close_column = [x.split(',')[7] for x in data][1:]
    result = [float(item) for item in close_column]
    len_x_test = int(len(result) * Ptest)
    len_x_train = int(len(result) * P0)
    relative_price = result[(len_x_train + (len_x_test * self.i)): len_x_train + len_x_test 
    * (self.i + 1)]
    return relative_price

def get_x_train(self):
    x = []
    for i in range(len(self.data_train)):
        if i + self.window < len(self.data_train):
            x.append(self.data_train[i: i + self.window])
    return x

def get_y_train(self):
    y = []
    for i in self.data_train[self.window:]:
        y += [i]
    return y

def get_x_test(self):
    x = []
    for i in range(len(self.data_test)):
        if i + self.window < len(self.data_test):
            x.append(self.data_test[i: i + self.window])
    return x

def get_y_test(self):
    y = []
    for i in self.data_test[self.window:]:
        y += [i]
    return y


class Linear_regression(Regression):

    def callculate(self):
    reg_linear = LinearRegression().fit(self.x_train, self.y_train)
    y_pred = reg_linear.predict(self.x_test)
    return y_pred


class Nearest_neighbor(Regression):

    def callculate(self):
    reg_neighbor = KNeighborsRegressor(n_neighbors=window, weights='distance')
    reg_neighbor.fit(self.x_train, self.y_train)
    y_pred = reg_neighbor.predict(self.x_test)
    return y_pred

window = 10
Pk = 1
P0 = 0.1
Ptest = 0.01
k = (Pk - P0)/Ptest
i = 0
y_real = []
y_neigh = []
y_lin = []
while i < k:
    lin_price = list(Linear_regression(window, P0, Ptest, i).callculate())
    neighbor = list(Nearest_neighbor(window, P0, Ptest, i).callculate())
    y_neigh.extend(neighbor)
    y_lin.extend(lin_price)
    y_real.extend(list(Linear_regression(window, P0, Ptest, i).y_test))
    i += 1

""" Output to graphs of the received data """

fig, ax = plt.subplots()
ax.plot(y_real, label='Initial data')
ax.plot(y_neigh, label='Nearest Neighbor Data')
ax.plot(y_lin, label='Linear Regression Data')
ax.set_xlabel('Time (min)')
ax.set_ylabel('Price, ($)')
ax.legend()
plt.show()

Answer 1

“线性回归预测良好”

不，它从来没有很好地预测过。 您只是查看了图表，并认为它看起来有点相似。 但是，如果您仔细观察，您的“模型”只是将之前的价格作为对现在价格的预测。 这意味着，它没有预测任何东西，它是一个历史设备。 不是预测设备。

这就是为什么如果你将这种“模型”反馈到自身中，你会得到一条直线：它总是预测下一个价格将等于上一个价格。