def calculate_ndiffs(self, alpha=0.05, test="kpss", max_d=2):
"""
Utility method for determining the optimal ``d`` value for ARIMA ordering. Calculating this
as a fixed value can dramatically increase the tuning time for ``pmdarima`` models.
:param alpha: significance level for determining if a pvalue used for testing a
value of ``'d'`` is significant or not.
Default: ``0.05``
:param test: Type of unit test for stationarity determination to use.
Supported values: ``['kpss', 'adf', 'pp']``
See:
https://alkaline-ml.com/pmdarima/modules/generated/pmdarima.arima.KPSSTest.\
html#pmdarima.arima.KPSSTest
https://alkaline-ml.com/pmdarima/modules/generated/pmdarima.arima.PPTest.\
html#pmdarima.arima.PPTest
https://alkaline-ml.com/pmdarima/modules/generated/pmdarima.arima.ADFTest.\
html#pmdarima.arima.ADFTest
Default: ``'kpss'``
:param max_d: The max value for ``d`` to test.
:return: Dictionary of ``{<group_key>: <optimal 'd' value>}``
"""
self._create_group_df()
group_ndiffs = {
group: ndiffs(x=group_df[self._y_col], alpha=alpha, test=test, max_d=max_d)
for group, group_df in self._group_df
}
return group_ndiffs
def check_stationarity(dataframe):
"""
Performs the Augmented Dickey-Fuller test on all the series constituting the dataframe given.
:param dataframe: dataframe to analyse with the ADF test.
"""
# Make sure that the original time series is not modified
data = dataframe.copy()
print('\nResults of Dickey-Fuller Test:')
print('{:<15}{:<15}{:<10}{:<10}'.format('Column', 'Stationary', 'P-value',
'Order'))
# Significance level to reject the null hypothesis is set to 0.05
adf_test = ADFTest(alpha=0.05)
# Cycle across each column of the dataframe
columns = dataframe.columns
for column in columns:
# If the series is already stationary the differencing order is equal to 0
order = 0
# Compute the ADF test. It returns the p-value and if the differencing is needed
results, should = adf_test.should_diff(data[column])
# If the series must be differenced
if should:
# The differencing order needed to transform the series in stationary is computed by ndiffs()
order = ndiffs(data[column], alpha=0.05)
print(f'{column:<15}{not should:<15}{results:<10.5f}{order:<10}')
def find_order(self, data):
# determine number of differences
kpss_diffs = ndiffs(data, alpha=0.05, test='kpss', max_d=6)
adf_diffs = ndiffs(data, alpha=0.05, test='adf', max_d=6)
n_diffs = max(kpss_diffs, adf_diffs)
# grid search to find order
self.order = pm.auto_arima(data,
d=n_diffs,
seasonal=False,
stepwise=True,
suppress_warnings=True,
error_action="ignore",
max_order=None,
trace=True,
maxiter=20).order
self.model = pm.arima.ARIMA(order=self.order)
return self
def fit_eval(self, data, validation_data, **config):
"""
Fit on the training data from scratch.
:param data: A 1-D numpy array as the training data
:param validation_data: A 1-D numpy array as the evaluation data
:return: the evaluation metric value
"""
if not self.model_init:
# Estimating differencing term (d) and seasonal differencing term (D)
kpss_diffs = ndiffs(data, alpha=0.05, test='kpss', max_d=6)
adf_diffs = ndiffs(data, alpha=0.05, test='adf', max_d=6)
d = max(adf_diffs, kpss_diffs)
D = 0 if not self.seasonal else nsdiffs(data, m=7, max_D=12)
config.update(d=d, D=D)
self._build(**config)
self.model_init = True
self.model.fit(data)
val_metric = self.evaluate(x=None, target=validation_data, metrics=[self.metric])[0].item()
return {self.metric: val_metric}
(1, 1),
(2, 0),
(2, 1)
]
for lag, ax_coords in enumerate(ax_idcs, 1):
ax_row, ax_col = ax_coords
axis = axes[ax_row][ax_col]
lag_plot(df['open'], lag=lag, ax=axis)
axis.set_title(f"Lag={lag}")
plt.show()
kpss_diffs = ndiffs(y_train, alpha=0.05, test='kpss', max_d=6)
adf_diffs = ndiffs(y_train, alpha=0.05, test='adf', max_d=6)
n_diffs = max(adf_diffs, kpss_diffs)
print(f"Estimated differencing term: {n_diffs}")
auto = pm.auto_arima(y_train, d=n_diffs, seasonal=False, stepwise=True,
suppress_warnings=True, error_action="ignore", max_p=6,
max_order=None, trace=True)
print(auto.order)
from sklearn.metrics import mean_squared_error
from pmdarima.metrics import smape
model = auto
Path("data").glob("forecast_*.json")),
ignore_index=True)
df["time"] = df.apply(lambda r: datetime.fromtimestamp(r["time"]), axis=1)
df = df.sort_values(by=["time"])
temperature = df["temperature"]
temperature = temperature.fillna(temperature.mean())
train, test = train_test_split(temperature,
train_size=temperature.shape[0] - 365)
print(f"training size: {train.shape[0]}")
print(f"testing size: {test.shape[0]}")
# %%
kpss_diffs = ndiffs(train, alpha=0.05, test="kpss", max_d=6)
adf_diffs = ndiffs(train, alpha=0.05, test="adf", max_d=6)
n_diffs = max(adf_diffs, kpss_diffs)
print(f"d: {n_diffs}")
# %%
model = auto_arima(
train,
d=n_diffs,
seasonal=True,
m=4,
stepwise=True,
suppress_warnings=True,
max_p=6,
trace=2,
from matplotlib import pyplot as plt
print("pmdarima version: %s" % pm.__version__)
# Load the data and split it into separate pieces
y, X = load_date_example()
y_train, y_test, X_train, X_test = \
model_selection.train_test_split(y, X, test_size=20)
# We can examine traits about the time series:
pm.tsdisplay(y_train, lag_max=10)
# We can see the ACF increases and decreases rather rapidly, which means we may
# need some differencing. There also does not appear to be an obvious seasonal
# trend.
n_diffs = arima.ndiffs(y_train, max_d=5)
# Here's what the featurizer will create for us:
date_feat = preprocessing.DateFeaturizer(
column_name="date", # the name of the date feature in the exog matrix
with_day_of_week=True,
with_day_of_month=True)
_, X_train_feats = date_feat.fit_transform(y_train, X_train)
print("Head of generated exog features:\n%s" % repr(X_train_feats.head()))
# We can plug this exog featurizer into a pipeline:
pipe = pipeline.Pipeline([
('date', date_feat),
('arima', arima.AutoARIMA(d=n_diffs,
trace=3,
# test['traffic'].plot(legend = True)
date_today = date.today().strftime('%Y-%m-%d')
date_tomorrow = max(traffic.index) + timedelta(days=1)
date_next_7d = pd.date_range(
start=date_tomorrow, periods=7,
freq='D').strftime(date_format='%Y-%m-%d').tolist()
future_forecast = pd.DataFrame({
'date': date_next_7d,
'traffic': future_forecast
}).assign(date_of_forecast=date_today)
future_forecast.loc[future_forecast['traffic'] < 0, 'traffic'] = 0
future_forecast['traffic'] = future_forecast['traffic'].astype(int)
engine = create_engine(
"mysql://*****:*****@etcinsights.nazwa.pl/etcinsights_harebakken"
)
future_forecast.to_sql('traffic_date_forecast_python',
con=engine,
if_exists='append',
index=True)
print('Data uploaded to db successfully!')
from pmdarima.arima import ndiffs
kpss_diffs = ndiffs(traffic, alpha=0.05, test='kpss', max_d=6)
adf_diffs = ndiffs(traffic, alpha=0.05, test='adf', max_d=6)
n_diffs = max(adf_diffs, kpss_diffs)
print(f"Estimated differencing term: {n_diffs}")
# Estimated differencing term: 1