def test_conf_int(X_y_linear_trend):
HORIZON = 5
X, y = X_y_linear_trend
model = TBATS(use_arma_errors=False, use_box_cox=False)
model_wrapped = TBATSWrapper(use_arma_errors=False,
use_box_cox=False,
conf_int=True,
conf_int_level=0.95)
model = model.fit(y[:-HORIZON])
model_wrapped = model_wrapped.fit(X[:-HORIZON], y[:-HORIZON])
preds_orig, conf_int = model.forecast(steps=HORIZON, confidence_level=0.95)
preds = model_wrapped.predict(X[-HORIZON:])
expected_result = (pd.DataFrame(
preds_orig, index=X.index[-HORIZON:],
columns=["TBATS"]).assign(TBATS_lower=conf_int["lower_bound"]).assign(
TBATS_upper=conf_int["upper_bound"]))
print("expected_result", expected_result)
print("preds", preds)
print("preds_orig", preds_orig)
assert_frame_equal(preds, expected_result)
class Tbats(base_model.BaseModel):
"""
Trigonometric seasonality, Box-Cox transformation, ARMA errors, Trend and Seasonal components.
"""
def _tune(self, y, period, x=None, metric="mse", val_size=None, verbose=False):
"""
Tune hyperparameters of the model.
:param y: pd.Series or 1-D np.array, time series to predict.
:param period: Int or Str, the number of observations per cycle: 1 or "annual" for yearly data, 4 or "quarterly"
for quarterly data, 7 or "daily" for daily data, 12 or "monthly" for monthly data, 24 or "hourly" for hourly
data, 52 or "weekly" for weekly data. First-letter abbreviations of strings work as well ("a", "q", "d", "m",
"h" and "w", respectively). Additional reference: https://robjhyndman.com/hyndsight/seasonal-periods/.
:param x: not used for TBATS model
:param metric: not used for TBATS model; model selection is based on the AIC.
:param val_size: Int, the number of most recent observations to use as validation set for tuning.
:param verbose: Boolean, True for printing additional info while tuning.
:return: None
"""
self.period = data_utils.period_to_int(period) if type(period) == str else period
self.model = TBATS(seasonal_periods=[period], show_warnings=False)
self.params["tuned"] = True
def fit(self, y, period, x=None, metric="mse", val_size=None, verbose=False):
"""
Build the model using best-tuned hyperparameter values.
:param y: pd.Series or 1-D np.array, time series to predict.
:param period: Int or Str, the number of observations per cycle: 1 or "annual" for yearly data, 4 or "quarterly"
for quarterly data, 7 or "daily" for daily data, 12 or "monthly" for monthly data, 24 or "hourly" for hourly
data, 52 or "weekly" for weekly data. First-letter abbreviations of strings work as well ("a", "q", "d", "m",
"h" and "w", respectively). Additional reference: https://robjhyndman.com/hyndsight/seasonal-periods/.
:param x: not used for TBATS model
:param metric: not used for TBATS model; model selection is based on the AIC.
:param val_size: not used for TBATS model; model selection is based on the AIC.
:param verbose: Boolean, True for printing additional info while tuning.
:return: None
"""
self.y = y
self.name = "TBATS"
self.key = "tbats"
self._tune(y=y, period=period, x=x, metric=metric, val_size=val_size, verbose=verbose)
self.model = self.model.fit(y)
def predict(self, horizon, x=None):
"""
Predict future values of the time series using the fitted model.
:param horizon: Int, the number of observations in the future to predict
:param x: not used for TBATS model
:return: 1-D np.array with predictions
"""
return self.model.forecast(steps=horizon)
class TBATSFF:
"""
TBATS wrapper
"""
def __init__(self):
pass
def fit(self, ts_init, frcy):
self.frcy = frcy
self.tbats = TBATS(seasonal_periods=[self.frcy, 7 * self.frcy],
use_arma_errors=False).fit(ts_init)
return self
def predict(self, h):
y_hat = self.tbats.forecast(steps=h)
return y_hat