使用 python 在 arima 中進行預測

Question

我像這樣創建了一個 ARIMA model

for i in range(len(testy)):
    model = ARIMA(endog=trainy,exog=trainx,order=(1,0,1))
    model_fit = model.fit()
    ll=testx[i]
    yhat = model_fit.forecast(exog=ll)
    pred.append(yhat[0])
    actual = testy[i]
    all_values.append(actual)
    #print('=%f\t=%f' % (yhat[0], actual))
    print('predicted=%f, expected=%f' % (yhat[0], actual))

現在我希望預測未來 12 周的未來價值。 是否可以？

# Forecast
start_index = len(df.values)
end_index = start_index + 6
forecast = model_fit.predict(start=start_index, end=end_index)
print(forecast)

當我使用上面的代碼片段時，出現如下所示的錯誤。

 ValueError: You must provide exog for ARMAX

編輯：使用的庫是：

import numpy as np
import pandas as pd
import datetime
from matplotlib import pyplot as plt
from numpy import log
from statsmodels.tsa.seasonal import seasonal_decompose
from statsmodels.tsa.stattools import adfuller
from scipy.stats import boxcox
from pandas.plotting import autocorrelation_plot
#from statsmodels.tsa.arima.model import ARIMA
from statsmodels.tsa.arima_model import ARIMA
from statsmodels.tsa.stattools import acf, pacf

Answer 1

事實上，方法 predict 沒有關鍵字參數n_periods使用start和end從ARIMA.predict的幫助你可以找到 arguments 的描述：

start : int, str, or datetime
    Zero-indexed observation number at which to start forecasting, ie.,
    the first forecast is start. Can also be a date string to
    parse or a datetime type.
end : int, str, or datetime
    Zero-indexed observation number at which to end forecasting, ie.,
    the first forecast is start. Can also be a date string to
    parse or a datetime type. However, if the dates index does not
    have a fixed frequency, end must be an integer index if you
    want out of sample prediction

這是一個很好的例子，說明如何進行多步預測。