BoxCoxEndogTransformer(lmbda2=0.4, neg_action="raise", floor=1e-12),
),
("arima", AutoARIMA(out_of_sample_size=60, max_p=4, max_q=4, max_d=4)),
]
)
pipeline_arima = GroupedPmdarima(model_template=pipeline_obj).fit(
df=training_data,
group_key_columns=group_key_columns,
y_col="y",
datetime_col="ds",
silence_warnings=True,
)
# Save to local directory
save_dir = "/tmp/group_pmdarima/pipeline.gpmd"
pipeline_arima.save(save_dir)
# Load from saved model
loaded_model = GroupedPmdarima.load(save_dir)
print("\nPipeline AutoARIMA results:\n", "-" * 40)
get_and_print_model_metrics_params(loaded_model)
print("\nPredictions:\n", "-" * 40)
prediction = loaded_model.predict(
n_periods=30, alpha=0.2, predict_col="predictions", return_conf_int=True
)
print(prediction.to_string())
print("\nCross validation metric results:\n", "-" * 40)
cross_validator = RollingForecastCV(h=30, step=365, initial=730)
training_data = generated_data.df
group_key_columns = generated_data.key_columns
# Build a GroupedPmdarima model by specifying an ARIMA model
arima_obj = ARIMA(order=(2, 1, 3), out_of_sample_size=60)
base_arima = GroupedPmdarima(model_template=arima_obj).fit(
df=training_data,
group_key_columns=group_key_columns,
y_col="y",
datetime_col="ds",
silence_warnings=True,
)
# Save to local directory
save_dir = "/tmp/group_pmdarima/arima.gpmd"
base_arima.save(save_dir)
# Load from saved model
loaded_model = GroupedPmdarima.load(save_dir)
print("\nARIMA results:\n", "-" * 40)
get_and_print_model_metrics_params(loaded_model)
prediction = loaded_model.predict(
n_periods=30, alpha=0.02, predict_col="forecast", return_conf_int=True
)
print("\nPredictions:\n", "-" * 40)
print(prediction.to_string())
print("\nCross validation metric results:\n", "-" * 40)
cross_validator = SlidingWindowForecastCV(h=90, step=365, window_size=730)
test="kpss",
max_d=4,
)
grouped_model = GroupedPmdarima(model_template=pipeline).fit(
df=training_data,
group_key_columns=group_key_columns,
y_col="y",
datetime_col="ds",
ndiffs=ndiff,
silence_warnings=True,
)
# Save to local directory
save_dir = "/tmp/group_pmdarima/pipeline_override.gpmd"
grouped_model.save(save_dir)
# Load from saved model
loaded_model = GroupedPmdarima.load(save_dir)
print("\nAutoARIMA results:\n", "-" * 40)
get_and_print_model_metrics_params(loaded_model)
print("\nPredictions:\n", "-" * 40)
prediction = loaded_model.predict(
n_periods=30, alpha=0.1, predict_col="forecasted_values", return_conf_int=True
)
print(prediction.to_string())
cv_evaluator = SlidingWindowForecastCV(h=90, step=120, window_size=180)
cross_validation = loaded_model.cross_validate(
