使用fit pmdarima ARIMA模型进行预测

Question

我可以使用pmdarima来拟合SARIMA模型。

import pmdarima as pm
from pmdarima.model_selection import train_test_split
import numpy as np
import matplotlib.pyplot as plt
# Load/split
y = pm.datasets.load_wineind()
train, test = train_test_split(y, train_size=150)
# Fit
model = pm.auto_arima(train, seasonal=True, m=12)

我可以从这些数据中做出预测，我甚至可以看到样本内预测，从中我可以计算残差。

N = test.shape[0]  # predict N steps into the future
forecasts = model.predict(N)
in_sample_forecasts = model.predict_in_sample()

但SARIMA只是一个数学模型(据我所知)。因此，我希望能够使用拟合的模型参数来预测其他一些系列。我可以这样做吗?

例如:

# Some other series entirely
some_other_series = train + np.random.randint(0, 5000, len(train))
# The following method does not exist but illustrates the desired functionality
forecasts = model.predict_for(some_other_series, N)

Answer 1

我已经找到了一个解决方案。诀窍是运行另一个拟合，但让优化器在底层基本上对已经拟合的参数执行无操作。我发现method='nm'实际上服从了maxiter=0，而其他的则不服从。下面是pmdarima模型的代码，但同样的想法也适用于statsmodels中的SARIMAX模型。

from copy import deepcopy
# Some other series entirely
some_other_series = train + np.random.randint(0, 5000, len(train))
# Deep copy original model for later comparison
new_model = deepcopy(model)
new_model.method = 'nm'
new_model.fit(some_other_series, maxiter=0, start_params=new_model.params())
new_model.params()
new_model.predict(12)
# Note that the params have stayed the same and predictions are different
model.params()
model.predict(12)