def train_model_autoarima(y, x, output: bool = True) -> AutoARIMA:
if output:
logger.info("Training AutoARIMA model...")
timer = Timer()
model = AutoARIMA(suppress_warnings=True, error_action='ignore')
y = pd.Series(data=np.delete(y, 0))
x = pd.DataFrame(data=x[:-1])
model.fit(y, x)
if output:
model.summary()
logger.info(f'Done in {timer}')
return model
plt.style.use('ggplot')
df = pd.read_csv(
'https://raw.githubusercontent.com/selva86/datasets/master/a10.csv',
parse_dates=['date'],
index_col="date")
df.index = pd.PeriodIndex(df.index, freq="M")
series = df.T.iloc[0]
plot_series(series)
model_auto = AutoARIMA(sp=12, suppress_warnings=True).fit(series)
summary = model_auto.summary()
def get_params(summary_text):
full = re.findall(r'SARIMAX\(.*?\)x\(.*?\)', summary_text)[0]
info = [int(_) for _ in re.findall(r'\d+', full)]
return info
p, d, q, P, D, Q, S = get_params(summary.as_text())
y_train, y_test = temporal_train_test_split(series, test_size=24)
fh = ForecastingHorizon(y_test.index, is_relative=False)
plot_series(y_train, y_test, labels=['Train', 'Test'])