ARIMA模型循环python

发布于 2025-01-11 08:45:39 字数 535 浏览 1 评论 0原文

我有一个在 train[:350] test[350:427] 值的数据集中训练的 arima 模型。我正在将模型拟合到训练值中，我的 (p,d,q) 值为 (1,1,2)。目前我一次只能预测一个时间步。我想循环运行模型，以便每次输出一个预测值时，都会将其添加到训练数据集中，并使用新值来预测另一个新的预测值。我无法理解如何做到这一点，到目前为止这就是我所得到的。

historical = train['max']
predictions = []

for t in range(len(test)):
    model = ARIMA(historical, order=(1,1,2))
    model_fit = model.fit()
    output = model_fit.forecast(exog=test['max'][t])
    predictions.append(output)
    observed = test['max'][t]
    historical.append(predictions)
    print(len(historical))

原文

I have an arima Model trained in a dataset of train[:350] test[350:427] values. I Am fitting the model in train values and my (p,d,q) values are (1,1,2). Currently i can predict only one time step at a time. I want to run the model in loop so that everytime it outputs one forecasted value, it is added to the train dataset and the new value is used to predict another new forecasted value. I am unable to understand how to do it and so far this is what i have got.

historical = train['max']
predictions = []

for t in range(len(test)):
    model = ARIMA(historical, order=(1,1,2))
    model_fit = model.fit()
    output = model_fit.forecast(exog=test['max'][t])
    predictions.append(output)
    observed = test['max'][t]
    historical.append(predictions)
    print(len(historical))

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