Advanced Sarimax Usage

If you are not a magician who can easily infer correct SARIMAX orders from looking on PACF (partial autocorrelation function) and ACF (autocorrelation function), you want to rather leverage AutoSarima which finds them for you - set init_with_autoarima to True.

If you want to further configure the search space of AutoARIMA, then you can provide all parameters of pmdarima.arima.AutoARIMA as autoarima_dict arguments.

When you run cross-validation with enabled AutoARIMA (init_with_autoarima), it’s often advisable to find the correct order only during the first fit call and reuse this model on all other splits in order to simulate the out-of-sample performance.

The signature of SarimaxWrapper contains parameters of pmdarima.arima.ARIMA, not AutoARIMA class.

For more parameters check pmdarima docs

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('seaborn')
plt.rcParams['figure.figsize'] = [12, 6]

from hcrystalball.utils import get_sales_data

df = get_sales_data(n_dates=100,
                    n_assortments=1,
                    n_states=1,
                    n_stores=1)
X, y = df[["Open"]], df['Sales']

X

	Open
Date
2015-04-23	True
2015-04-24	True
2015-04-25	True
2015-04-26	False
2015-04-27	True
...	...
2015-07-27	True
2015-07-28	True
2015-07-29	True
2015-07-30	True
2015-07-31	True

100 rows × 1 columns

from hcrystalball.wrappers import SarimaxWrapper

SarimaxWrapper?

model = SarimaxWrapper(
    autoarima_dict={'d':1, 'm':7, 'max_p':2, 'max_q':2},
    init_with_autoarima=True
)

preds = (model.fit(X[:-10], y[:-10])
         .predict(X[-10:])
         .merge(y, left_index=True, right_index=True, how='outer')
         .tail(50)
)
preds.plot(title=f"MAE:{(preds['Sales']-preds['sarimax']).abs().mean().round(3)}");

<AxesSubplot:title={'center':'MAE:3151.698'}, xlabel='Date'>

And now access the models parameters

model

SarimaxWrapper(always_search_model=False,
               autoarima_dict={'d': 1, 'm': 7, 'max_p': 2, 'max_q': 2},
               clip_predictions_lower=None, clip_predictions_upper=None,
               conf_int=False, hcb_verbose=True, init_with_autoarima=False,
               maxiter=50, method='lbfgs', name='sarimax', order=(0, 1, 2),
               out_of_sample_size=0, scoring='mse', scoring_args={},
               seasonal_order=(0, 0, 2, 7), start_params=None,
               suppress_warnings=True, trend=None, with_intercept=False)

You might also directly pass the orders if you know, what are you doing

model = SarimaxWrapper(order=(1, 1, 2), seasonal_order=(1, 0, 2, 7))

preds = (model.fit(X[:-10], y[:-10])
         .predict(X[-10:])
         .merge(y, left_index=True, right_index=True, how='outer')
         .tail(50)
)
preds.plot(title=f"MAE:{(preds['Sales']-preds['sarimax']).abs().mean().round(3)}");

<AxesSubplot:title={'center':'MAE:2154.027'}, xlabel='Date'>

model

SarimaxWrapper(always_search_model=False, autoarima_dict=None,
               clip_predictions_lower=None, clip_predictions_upper=None,
               conf_int=False, hcb_verbose=True, init_with_autoarima=False,
               maxiter=50, method='lbfgs', name='sarimax', order=(1, 1, 2),
               out_of_sample_size=0, scoring='mse', scoring_args=None,
               seasonal_order=(1, 0, 2, 7), start_params=None,
               suppress_warnings=False, trend=None, with_intercept=True)

model

SarimaxWrapper(always_search_model=False, autoarima_dict=None,
               clip_predictions_lower=None, clip_predictions_upper=None,
               conf_int=False, hcb_verbose=True, init_with_autoarima=False,
               maxiter=50, method='lbfgs', name='sarimax', order=(1, 1, 2),
               out_of_sample_size=0, scoring='mse', scoring_args=None,
               seasonal_order=(1, 0, 2, 7), start_params=None,
               suppress_warnings=False, trend=None, with_intercept=True)