我已将拦截系数,AR,MA存储在statsmodel包的ARIMA模型中
x = df_sku
x_train = x['Weekly_Volume_Sales']
x_train_log = np.log(x_train)
x_train_log[x_train_log == -np.inf] = 0
x_train_mat = x_train_log.as_matrix()
model = ARIMA(x_train_mat, order=(1,1,1))
model_fit = model.fit(disp=0)
res = model_fit.predict(start=1, end=137, exog=None, dynamic=False)
print(res)
params = model_fit.params
但是我无法找到有关statsmodel的任何文档,它允许我将模型参数重新设置为一组新数据并预测N个步骤。
有没有人能够完成改装模型并预测不合时宜的样品?
我试图完成类似于R的事情:
# Refit the old model with testData
new_model <- Arima(as.ts(testData.zoo), model = old_model)
答案 0 :(得分:1)
这是您可以使用的代码:
def ARIMAForecasting(data, best_pdq, start_params, step):
model = ARIMA(data, order=best_pdq)
model_fit = model.fit(start_params = start_params)
prediction = model_fit.forecast(steps=step)[0]
#This returns only last step
return prediction[-1], model_fit.params
#Get the starting parameters on train data
best_pdq = (3,1,3) #It is fixed, but you can search for the best parameters
model = ARIMA(train_data, best_pdq)
model_fit = model.fit()
start_params = model_fit.params
data = train_data
predictions = list()
for t in range(len(test_data)):
real_value = data[t]
prediction = ARIMAForecasting(data, best_pdq, start_params)
predictions.append(prediction)
data.append(real_value)
#After you can compare test_data with predictions
您可以在此处查看详细信息: https://www.statsmodels.org/dev/generated/statsmodels.tsa.arima_model.ARIMA.fit.html#statsmodels.tsa.arima_model.ARIMA.fit