def fallback(mase_all, smape_all, train, test, m):
naive = NaiveSeasonal(K=m)
naive.fit(train)
forecast = naive.predict(len(test))
mase_all.append(np.vstack([
mase_m4(train, test, forecast, m=m),
]))
smape_all.append(np.vstack([
smape_m4(test, forecast),
]))
def test_predict_ensemble_local_models(self):
naive = NaiveSeasonal(K=5)
theta = Theta()
naive_ensemble = NaiveEnsembleModel([naive, theta])
naive_ensemble.fit(self.series1 + self.series2)
forecast_naive_ensemble = naive_ensemble.predict(5)
naive.fit(self.series1 + self.series2)
theta.fit(self.series1 + self.series2)
forecast_mean = 0.5 * naive.predict(5) + 0.5 * theta.predict(5)
self.assertTrue(
np.array_equal(forecast_naive_ensemble.values(),
forecast_mean.values()))
def naive2_groe(ts: TimeSeries, n: int, m: int):
"""
Return the prediction of the naive2 baseline
"""
# It will be better to use R functions
ts_des = ts
seasonOut = 1
if m > 1:
if check_seasonality(ts, m=int(m), max_lag=2 * m):
_, season = extract_trend_and_seasonality(ts, m, model=ModelMode.MULTIPLICATIVE)
ts_des = remove_from_series(ts, season, model=ModelMode.MULTIPLICATIVE)
seasonOut = season[-m:].shift(m)
seasonOut = seasonOut.append_values(seasonOut.values())[:n]
naive2 = NaiveSeasonal(K=1)
naive2.fit(ts_des)
return naive2.predict(n) * seasonOut
seasonal_periods=m)
naive.fit(train)
naive2.fit(train_des)
naiveSeason.fit(train)
try:
ses.fit(train_des)
except ValueError:
# not useful anymore
train_des = train_des.shift(-len(train_des)).append(train_des)
# train_des = TimeSeries.from_times_and_values(train_des.shift(-11).time_index[:11],
# 2*train_des.values()[0]-train_des.values()[10::-1]) \
# .append(train_des)
ses.fit(train_des)
holt.fit(train_des)
damp.fit(train_des)
forecast_naiveSeason = naiveSeason.predict(len(test))
forecast_naiveDrift = naive.predict(len(test))
forecast_naive = forecast_naiveSeason + forecast_naiveDrift - train.last_value(
)
forecast_naive2 = naive2.predict(len(test)) * seasonOut
forecast_ses = ses.predict(len(test)) * seasonOut
forecast_holt = holt.predict(len(test)) * seasonOut
forecast_damp = damp.predict(len(test)) * seasonOut
forecast_comb = ((forecast_ses + forecast_holt + forecast_damp) /
3)
mase_all.append(
np.vstack([
mase_m4(train, test, forecast_naiveSeason, m=m),
mase_m4(train, test, forecast_naive, m=m),
mase_m4(train, test, forecast_naive2, m=m),