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我可以使用 SARIMA 模型拟合一些数据pmdarima。

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)

我可以根据这些数据进行预测，甚至可以查看样本内预测，从中可以计算残差。

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）。所以我希望能够使用拟合的模型参数来完全预测其他一些系列。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)
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1 回答 1

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我已经找到了解决方案。诀窍是运行另一个拟合，但让优化器在引擎盖下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，而其他人则没有。下面是pmdarima模型的代码，但同样的想法也适用SARIMAX于statsmodels.

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)
于 2021-09-09T04:48:02.283 回答