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
naive = NaiveDrift()
naive2 = NaiveSeasonal(K=1)
naiveSeason = NaiveSeasonal(K=m)
ses = ExponentialSmoothing(trend=None,
seasonal=None,
seasonal_periods=m)
holt = ExponentialSmoothing(seasonal=None,
damped=False,
trend='additive',
seasonal_periods=m)
damp = ExponentialSmoothing(seasonal=None,
damped=True,
trend='additive',
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(
train_des = remove_from_series(
train, season, model=ModelMode.MULTIPLICATIVE)
seasonOut = season[-m:].shift(m)
seasonOut = seasonOut.append_values(seasonOut.values())
seasonOut = seasonOut[:len(test)]
season_diff.append(
np.abs(train_des.values() - r_train_des) / np.abs(r_train_des))
train_des = TimeSeries.from_times_and_values(
train.time_index(), r_train_des)
seasonOut = TimeSeries.from_times_and_values(
test.time_index(), r_seasonOut)
naive2 = NaiveSeasonal(K=1)
naive2.fit(train_des)
forecast_naive2 = naive2.predict(len(test)) * seasonOut
mase_all.append(
np.vstack([
mase_m4(train, test, forecast_naive2, m=m),
]))
smape_all.append(np.vstack([
smape_m4(test, forecast_naive2),
]))
rel_error = np.mean(np.hstack(season_diff))
pkl.dump([mase_all, smape_all, season_diff],
open("rnaive2_" + cat + ".pkl", "wb"))
print(
np.round(np.nanmean(np.stack(mase_all), axis=(