1.solver：采用哪一种收敛算法（不建议更改
2.disp：True会打印中间过程，我们直接设置False即可
3.transparam：默认是True，建议选择true
Whether or not to transform the parameters to ensure stationarity. Uses the transformation suggested in Jones (1980). If False, no checking for stationarity or invertibility is done.
4.trend：=‘c'表示加一个截距项，=’nc‘表示不加截距项
举个例子
from pandas import read_csv
from pandas import datetime
from matplotlib import pyplot
from statsmodels.tsa.arima_model import ARIMA
from sklearn.metrics import mean_squared_error
from math import sqrt
# load dataset
def parser(x):
return datetime.strptime('190'+x, '%Y-%m')
series = read_csv('shampoo-sales.csv', header=0, parse_dates=[0], index_col=0, squeeze=True, date_parser=parser)
# split into train and test sets
X = series.values
size = int(len(X) * 0.66)
train, test = X[0:size], X[size:len(X)]
history = [x for x in train]
predictions = list()
# walk-forward validation
for t in range(len(test)):
# fit model
model = ARIMA(history, order=(4,1,0))
model_fit = model.fit(disp=False, trend='c')
print(model_fit.params)
# one step forecast
yhat = model_fit.forecast()[0]
# store forecast and ob
predictions.append(yhat)
history.append(test[t])
# evaluate forecasts
rmse = sqrt(mean_squared_error(test, predictions))
print('Test RMSE: %.3f' % rmse)from pandas import read_csv
from pandas import datetime
from matplotlib import pyplot
from statsmodels.tsa.arima_model import ARIMA
from sklearn.metrics import mean_squared_error
from math import sqrt
# load dataset
def parser(x):
return datetime.strptime('190'+x, '%Y-%m')
series = read_csv('shampoo-sales.csv', header=0, parse_dates=[0], index_col=0, squeeze=True, date_parser=parser)
# split into train and test sets
X = series.values
size = int(len(X) * 0.66)
train, test = X[0:size], X[size:len(X)]
history = [x for x in train]
predictions = list()
# walk-forward validation
for t in range(len(test)):
# fit model
model = ARIMA(history, order=(4,1,0))
model_fit = model.fit(disp=False, trend='nc')
print(model_fit.params)
# one step forecast
yhat = model_fit.forecast()[0]
# store forecast and ob
predictions.append(yhat)
history.append(test[t])
# evaluate forecasts
rmse = sqrt(mean_squared_error(test, predictions))
print('Test RMSE: %.3f' % rmse)加截距项的结果：
...
[ 11.42283717 -1.16087885 -0.6519841 -0.547411 -0.28820764]
[ 11.75656838 -1.11443479 -0.61607471 -0.49084722 -0.24452864]
[ 11.40486702 -1.11705478 -0.65344924 -0.50213939 -0.25677931]
Test RMSE: 81.545
不加截距项的结果：
...
[-0.90717131 -0.22332019 -0.11240858 -0.04008561]
[-0.88836083 -0.21098412 -0.09046333 -0.02121404]
[-0.89260136 -0.24120301 -0.10243393 -0.03165432]
Test RMSE: 95.061
选哪一个有你自己决定
https://machinelearningmastery.com/tune-arima-parameters-python/
这一个文章有时序经典各种模型的cheatsheethttps://machinelearningmastery.com/time-series-forecasting-methods-in-python-cheat-sheet/