如何使用拟合模型预测未来的价值？

Question

我正在学习时间序列预测。这是代码，它运行良好，预测总是与测试数据进行比较。现在假设今天的股价是X，我如何预测明天的股价Y（MSFT）？这是预测的主要目标。谢谢

import numpy as np
import pandas as pd
import pandas_datareader.data as web
# Error Metrics
from sklearn.metrics import mean_squared_error
# Time series Models
from statsmodels.tsa.arima_model import ARIMA
from matplotlib import pyplot
from datetime import datetime, timedelta
#Diable the warnings
import warnings
warnings.filterwarnings('ignore')

stk_tickers = ['MSFT', 'IBM', 'GOOGL']
ccy_tickers = ['DEXJPUS', 'DEXUSUK']
idx_tickers = ['SP500', 'DJIA', 'VIXCLS']

stk_data = web.DataReader(stk_tickers, 'yahoo',start_date, end_date)
ccy_data = web.DataReader(ccy_tickers, 'fred')
idx_data = web.DataReader(idx_tickers, 'fred')

return_period = 5
Y = np.log(stk_data.loc[:, ('Adj Close', 'MSFT')]).diff(return_period).shift(-return_period)
Y.name = Y.name[-1]+'_pred'

X1 = np.log(stk_data.loc[:, ('Adj Close', ('GOOGL', 'IBM'))]).diff(return_period)
X1.columns = X1.columns.droplevel()
X2 = np.log(ccy_data).diff(return_period)
X3 = np.log(idx_data).diff(return_period)

X4 = pd.concat([np.log(stk_data.loc[:, ('Adj Close', 'MSFT')]).diff(i) for i in [return_period, return_period*3, return_period*6, return_period*12]], axis=1).dropna()
X4.columns = ['MSFT_DT', 'MSFT_3DT', 'MSFT_6DT', 'MSFT_12DT']

X = pd.concat([X1, X2, X3, X4], axis=1)

dataset = pd.concat([Y, X], axis=1).dropna().iloc[::return_period, :]
Y = dataset.loc[:, Y.name]
X = dataset.loc[:, X.columns]

validation_size = 0.2

#In case the data is not dependent on the time series, then train and test split randomly
# seed = 7
# X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=validation_size, random_state=seed)

#In case the data is not dependent on the time series, then train and test split should be done based on sequential sample
#This can be done by selecting an arbitrary split point in the ordered list of observations and creating two new datasets.
train_size = int(len(X) * (1-validation_size))
X_train, X_test = X[0:train_size], X[train_size:len(X)]
Y_train, Y_test = Y[0:train_size], Y[train_size:len(X)]

X_train_ARIMA=X_train.loc[:, ['GOOGL', 'IBM', 'DEXJPUS', 'SP500', 'DJIA', 'VIXCLS']]
X_test_ARIMA=X_test.loc[:, ['GOOGL', 'IBM', 'DEXJPUS', 'SP500', 'DJIA', 'VIXCLS']]
tr_len = len(X_train_ARIMA)
te_len = len(X_test_ARIMA)
to_len = len (X)

modelARIMA=ARIMA(endog=Y_train,exog=X_train_ARIMA,order=[2,0,1])
model_fit = modelARIMA.fit()

error_Training_ARIMA = mean_squared_error(Y_train, model_fit.fittedvalues)
predicted = model_fit.predict(start = tr_len -1 ,end = to_len -1, exog = X_test_ARIMA)[1:]