如何使用 sklearn python 预测未来的数据帧？

Question

我正在运行此链接中的示例。

经过几次修改，我已经成功运行了代码。下面是修改后的代码：

import quandl, math
import numpy as np
import pandas as pd
from sklearn import preprocessing, cross_validation, svm
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
from matplotlib import style
import datetime

style.use('ggplot')

df = quandl.get("WIKI/GOOGL")
df = df[['Adj. Open',  'Adj. High',  'Adj. Low',  'Adj. Close', 'Adj. Volume']]
df['HL_PCT'] = (df['Adj. High'] - df['Adj. Low']) / df['Adj. Close'] * 100.0
df['PCT_change'] = (df['Adj. Close'] - df['Adj. Open']) / df['Adj. Open'] * 100.0

df = df[['Adj. Close', 'HL_PCT', 'PCT_change', 'Adj. Volume']]
forecast_col = 'Adj. Close'
df.fillna(value=-99999, inplace=True)
forecast_out = int(math.ceil(0.01 * len(df)))
df['label'] = df[forecast_col].shift(-forecast_out)

X = np.array(df.drop(['label'], 1))
X = preprocessing.scale(X)
X_lately = X[-forecast_out:]
X = X[:-forecast_out]

df.dropna(inplace=True)

y = np.array(df['label'])

X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.2)
clf = LinearRegression(n_jobs=-1)
clf.fit(X_train, y_train)
confidence = clf.score(X_test, y_test)

forecast_set = clf.predict(X_lately)
df['Forecast'] = np.nan

last_date = df.iloc[-1].name
last_unix = last_date.timestamp()
one_day = 86400
next_unix = last_unix + one_day

for i in forecast_set:
    next_date = datetime.datetime.fromtimestamp(next_unix)
    next_unix += 86400
    df.loc[next_date] = [np.nan for _ in range(len(df.columns)-1)]+[i]

df['Adj. Close'].plot()
df['Forecast'].plot()
plt.legend(loc=4)
plt.xlabel('Date')
plt.ylabel('Price')
plt.show()

但我面临的问题是对未来数据框的预测。这是输出图像：

如图所示，我一直到 2017-2018 年。如何进一步迈向 2019、2020 或 5 年后？

Answer 1

您的代码使用此 DataFrame 作为X生成预测：

df = df[['Adj. Close', 'HL_PCT', 'PCT_change', 'Adj. Volume']]

这意味着，如果您想预测未来五年的价格，您将需要这些['Adj. Close', 'HL_PCT', 'PCT_change', 'Adj. Volume']数据点来获取未来值，以便在更远的地方进行预测。

请注意，您图像中的预测是根据历史数据创建的，在此处作为测试集分离：X_lately = X[-forecast_out:]。所以它有预测的每个点都使用历史数据来预测未来的某个点。

如果您真的想使用此模型来预测未来 5 年，您首先需要预测/计算所有这些变量：predicted_X = ['Adj. Close', 'HL_PCT', 'PCT_change', 'Adj. Volume']，并继续使用clf.predict(predicted_X)inside运行一些循环。

我相信这门优达学城交易机器学习课程可能对你来说是一个很好的资源，它会给你一个更好的框架和心态来解决这类问题。

我希望我的回答对你来说是清楚和有帮助的，如果不是让我知道，我会澄清它或回答其他问题。

按照我所说的更新您的模型：

import quandl
import numpy as np
from sklearn import preprocessing, model_selection
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
from matplotlib import style
import datetime

style.use('ggplot')

df = quandl.get("WIKI/GOOGL")
df = df[['Adj. Open', 'Adj. High', 'Adj. Low', 'Adj. Close', 'Adj. Volume']]
df['HL_PCT'] = (df['Adj. High'] - df['Adj. Low']) / df['Adj. Close'] * 100.0
df['PCT_change'] = (df['Adj. Close'] - df['Adj. Open']) / df['Adj. Open'] * 100.0

df = df[['Adj. Close', 'HL_PCT', 'PCT_change', 'Adj. Volume']]
forecast_col = 'Adj. Close'
df.fillna(value=-99999, inplace=True)
forecast_out = 1
df['label'] = df[forecast_col].shift(-forecast_out)

X = np.array(df.drop(['label'], 1))
X = preprocessing.scale(X)
X_lately = X[-forecast_out:]
X = X[:-forecast_out]

df.dropna(inplace=True)

y = np.array(df['label'])

X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.2)

# Instantiate regressors
reg_close = LinearRegression(n_jobs=-1)
reg_close.fit(X_train, y_train)

reg_hl = LinearRegression(n_jobs=-1)
reg_hl.fit(X_train, y_train)

reg_pct = LinearRegression(n_jobs=-1)
reg_pct.fit(X_train, y_train)

reg_vol = LinearRegression(n_jobs=-1)
reg_vol.fit(X_train, y_train)

# Prepare variables for loop
last_close = df['Adj. Close'][-1]
last_date = df.iloc[-1].name.timestamp()
df['Forecast'] = np.nan
predictions_arr = X_lately

for i in range(100):
    # Predict next point in time
    last_close_prediction = reg_close.predict(predictions_arr)
    last_hl_prediction = reg_hl.predict(predictions_arr)
    last_pct_prediction = reg_pct.predict(predictions_arr)
    last_vol_prediction = reg_vol.predict(predictions_arr)

    # Create np.Array of current predictions to serve as input for future predictions
    predictions_arr = np.array((last_close_prediction, last_hl_prediction, last_pct_prediction, last_vol_prediction)).T
    next_date = datetime.datetime.fromtimestamp(last_date)
    last_date += 86400

    # Outputs data into DataFrame to enable plotting
    df.loc[next_date] = [np.nan, np.nan, np.nan, np.nan, np.nan, float(last_close_prediction)]

df['Adj. Close'].plot()
df['Forecast'].plot()
plt.legend(loc=4)
plt.xlabel('Date')
plt.ylabel('Price')
plt.show()

这个模型不是很有用，因为它很快就会向上爆炸，但是它的实现中有一些有趣和不寻常的东西。

为了对未来价格进行更现实的预测，您还需要实施某种随机游走。

您还可以使用不同的模型而不是LinearRegression诸如RandomForestRegressor，这会产生非常不同的结果。

from sklearn.ensemble import RandomForestRegressor

clf_close = RandomForestRegressor(n_jobs=-1)
clf_close.fit(X_train, y_train)

clf_hl = RandomForestRegressor(n_jobs=-1)
clf_hl.fit(X_train, y_train)

clf_pct = RandomForestRegressor(n_jobs=-1)
clf_pct.fit(X_train, y_train)

clf_vol = RandomForestRegressor(n_jobs=-1)
clf_vol.fit(X_train, y_train)

与预测价格不同，在给定某些进入参数和退出参数的情况下，预测特定头寸（买入或卖出）是否有利可图通常是一种更好的方法。在Udacity课程涵盖了这种方法。

随机游走模型：

import quandl
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import style
import datetime
import random

style.use('ggplot')

df = quandl.get("WIKI/GOOGL")
df = df[['Adj. Close']]

df.dropna(inplace=True)

# Prepare variables for loop

last_close = df['Adj. Close'][-1]
last_date = df.iloc[-1].name.timestamp()
df['Forecast'] = np.nan

for i in range(1000):
    # Create np.Array of current predictions to serve as input for future predictions
    modifier = random.randint(-100, 105) / 10000 + 1
    last_close *= modifier
    next_date = datetime.datetime.fromtimestamp(last_date)
    last_date += 86400

    # Outputs data into DataFrame to enable plotting
    df.loc[next_date] = [np.nan, last_close]

df['Adj. Close'].plot()
df['Forecast'].plot()
plt.legend(loc=4)
plt.xlabel('Date')
plt.ylabel('Price')
plt.show()

随机游走的输出图像