Automation and saving

ARIMA Models in Python

James Fulton

Climate informatics researcher

Searching over model orders

import pmdarima as pm

results = pm.auto_arima(df)

Performing stepwise search to minimize aic
 ARIMA(2,0,2)(1,1,1)[12] intercept   : AIC=inf, Time=3.33 sec
 ARIMA(0,0,0)(0,1,0)[12] intercept   : AIC=2648.467, Time=0.062 sec
 ARIMA(1,0,0)(1,1,0)[12] intercept   : AIC=2279.986, Time=1.171 sec

 ...

 ARIMA(3,0,3)(1,1,1)[12] intercept   : AIC=2173.508, Time=12.487 sec
 ARIMA(3,0,3)(0,1,0)[12] intercept   : AIC=2297.305, Time=2.087 sec

Best model:  ARIMA(3,0,3)(1,1,1)[12]
Total fit time: 245.812 seconds

pmdarima results

print(results.summary())

results.plot_diagnostics()

Non-seasonal search parameters

results = pm.auto_arima( df,             # data

                         d=0,            # non-seasonal difference order

                         start_p=1,      # initial guess for p
                         start_q=1,      # initial guess for q

                         max_p=3,        # max value of p to test
                         max_q=3,        # max value of q to test
                        )

¹ https://www.alkaline-ml.com/pmdarima/modules/generated/pmdarima.arima.auto_arima.html

Seasonal search parameters

results = pm.auto_arima( df,             # data
                         ... ,          # non-seasonal arguments 
                         seasonal=True,  # is the time series seasonal

                         m=7,            # the seasonal period

                         D=1,            # seasonal difference order

                         start_P=1,      # initial guess for P
                         start_Q=1,      # initial guess for Q

                         max_P=2,        # max value of P to test
                         max_Q=2,        # max value of Q to test
                        )

Other parameters

results = pm.auto_arima( df,                     # data    
                         ... ,                   # model order parameters     

                         information_criterion='aic', # used to select best model

                         trace=True,             # print results whilst training

                         error_action='ignore',  # ignore orders that don't work

                         stepwise=True,          # apply intelligent order search
                       )

Saving model objects

# Import
import joblib

# Select a filepath
filepath ='localpath/great_model.pkl'

# Save model to filepath
joblib.dump(model_results_object, filepath)

Saving model objects

# Select a filepath
filepath ='localpath/great_model.pkl'

# Load model object from filepath
model_results_object = joblib.load(filepath)

Updating model

# Add new observations and update parameters
model_results_object.update(df_new)

Update comparison

Let's practice!

ARIMA Models in Python