Scikit learn python中ARIMA的拟合值

我正在研究时间序列模型。我在金字塔arima模块中使用了自动arima模型。我已经在我的数据集上安装了auto_arima模型。现在我有两个问题

我想看看模型参数

我想从模型中得到拟合值

下面是我的示例代码

m1_hist = auto_arima(ts1_hist, start_p=1, start_q=1,
                       max_p=3, max_q=3, m=12,
                       start_P=0, seasonal=True,
                       d=1, D=1, trace=True,
                       error_action='ignore',  
                       suppress_warnings=True, 
                       stepwise=True)

m1_hist2 = m1_hist.fit(ts1_hist)

我使用

m1_hist.params

获取模型参数。但它没有向我展示输出

你能回答我的问题吗

---

提前谢谢。

实际上你应该使用

m1_hist.arparams()
# output: array([-0.06322811,  0.26664419]) in my case

或

---

找到模型后，应根据实际（y）值对其进行拟合。基于arima中所选模型的y值预测将为拟合值

比如说,

start_index = 0 
end_index = 15
forecast_index = 15
y = df.iloc[start_index:end_index] # end index in iloc is exclusive
model = auto_arima(y, ....)

# Predictions of y values based on "model", namely fitted values
yhat = model_fit.predict_in_sample(start=start_ind, end=end_ind - 1)

# One step forecast: forecast the element at index 15 
forecast = model_fit.predict(n_periods=1)

start_index = 0 
end_index = 15
forecast_index = 15
y = df.iloc[start_index:end_index] # end index in iloc is exclusive
model = auto_arima(y, ....)

# Predictions of y values based on "model", namely fitted values
yhat = model_fit.predict_in_sample(start=start_ind, end=end_ind - 1)

# One step forecast: forecast the element at index 15 
forecast = model_fit.predict(n_periods=1)