def test_extract(self):
# test default (naive) method
calc_trend, _ = extract_trend_and_seasonality(self.ts, freq=6)
diff = self.trend - calc_trend
self.assertTrue(np.isclose(np.mean(diff.values()**2), 0.0))
# test default (naive) method additive
calc_trend, _ = extract_trend_and_seasonality(self.ts,
freq=6,
model=ModelMode.ADDITIVE)
diff = self.trend - calc_trend
self.assertTrue(np.isclose(np.mean(diff.values()**2), 0.0))
# test STL method
calc_trend, _ = extract_trend_and_seasonality(self.ts,
freq=6,
method="STL",
model=ModelMode.ADDITIVE)
diff = self.trend - calc_trend
self.assertTrue(np.isclose(np.mean(diff.values()**2), 0.0))
# check if error is raised
with self.assertRaises(ValueError):
calc_trend, _ = extract_trend_and_seasonality(
self.ts, freq=6, method="STL", model=ModelMode.MULTIPLICATIVE)
def fit(self, train: TimeSeries):
super().fit(train)
train_des = train
self.seasonOut = 1
if self.m > 1:
if check_seasonality(train, m=self.m, max_lag=2 * self.m):
_, season = extract_trend_and_seasonality(train, self.m, model=ModelMode.MULTIPLICATIVE)
train_des = remove_from_series(train, season, model=ModelMode.MULTIPLICATIVE)
seasonOut = season[-self.m:].shift(self.m)
self.seasonOut = seasonOut.append_values(seasonOut.values())
self.model.fit(train_des)
def fit(self, series: TimeSeries):
super().fit(series)
ts = self.training_series
self.length = len(ts)
# Check for statistical significance of user-defined season period
# or infers season_period from the TimeSeries itself.
if self.season_mode is SeasonalityMode.NONE:
self.season_period = 1
else:
self.season_period = self.seasonality_period
if self.season_period is None:
max_lag = len(ts) // 2
self.is_seasonal, self.season_period = check_seasonality(
ts, self.season_period, max_lag=max_lag
)
else:
# force the user-defined seasonality to be considered as a true seasonal period.
self.is_seasonal = self.season_period > 1
new_ts = ts
# Store and remove seasonality effect if there is any.
if self.is_seasonal:
_, self.seasonality = extract_trend_and_seasonality(
ts, self.season_period, model=self.season_mode
)
new_ts = remove_from_series(ts, self.seasonality, model=self.season_mode)
# SES part of the decomposition.
self.model = hw.SimpleExpSmoothing(new_ts.values(copy=False)).fit()
# Linear Regression part of the decomposition. We select the degree one coefficient.
b_theta = np.polyfit(
np.array([i for i in range(0, self.length)]),
(1.0 - self.theta) * new_ts.values(copy=False),
1,
)[0]
# Normalization of the coefficient b_theta.
self.coef = b_theta / (-self.theta)
self.alpha = self.model.params["smoothing_level"]
if self.alpha == 0.0:
self.model = hw.SimpleExpSmoothing(new_ts.values(copy=False)).fit(
initial_level=ALPHA_START
)
self.alpha = self.model.params["smoothing_level"]
return self
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
def fit(self, series):
super().fit(series)
self.length = len(series)
# normalization of data
if self.normalization:
self.mean = series.pd_dataframe(copy=False).mean().mean()
raise_if_not(
not np.isclose(self.mean, 0),
"The mean value of the provided series is too close to zero to perform normalization",
logger,
)
new_ts = series / self.mean
else:
new_ts = series
# Check for statistical significance of user-defined season period
# or infers season_period from the TimeSeries itself.
if self.season_mode is SeasonalityMode.NONE:
self.season_period = 1
else:
self.season_period = self.seasonality_period
if self.season_period is None:
max_lag = len(series) // 2
self.is_seasonal, self.season_period = check_seasonality(
series, self.season_period, max_lag=max_lag
)
else:
# force the user-defined seasonality to be considered as a true seasonal period.
self.is_seasonal = self.season_period > 1
# Store and remove seasonality effect if there is any.
if self.is_seasonal:
_, self.seasonality = extract_trend_and_seasonality(
new_ts, self.season_period, model=self.season_mode
)
new_ts = remove_from_series(
new_ts, self.seasonality, model=self.season_mode
)
ts_values = new_ts.univariate_values()
if (ts_values <= 0).any():
self.model_mode = ModelMode.ADDITIVE
self.trend_mode = TrendMode.LINEAR
logger.warning(
"Time series has negative values. Fallback to additive and linear model"
)
# Drift part of the decomposition
if self.trend_mode is TrendMode.LINEAR:
linreg = ts_values
else:
linreg = np.log(ts_values)
self.drift = np.poly1d(np.polyfit(np.arange(self.length), linreg, 1))
theta0_in = self.drift(np.arange(self.length))
if self.trend_mode is TrendMode.EXPONENTIAL:
theta0_in = np.exp(theta0_in)
if (theta0_in > 0).all() and self.model_mode is ModelMode.MULTIPLICATIVE:
theta_t = (ts_values**self.theta) * (theta0_in ** (1 - self.theta))
else:
if self.model_mode is ModelMode.MULTIPLICATIVE:
logger.warning("Negative Theta line. Fallback to additive model")
self.model_mode = ModelMode.ADDITIVE
theta_t = self.theta * ts_values + (1 - self.theta) * theta0_in
# SES part of the decomposition.
self.model = hw.SimpleExpSmoothing(theta_t).fit()
theta2_in = self.model.fittedvalues
if (theta2_in > 0).all() and self.model_mode is ModelMode.MULTIPLICATIVE:
self.fitted_values = theta2_in**self.wses * theta0_in**self.wdrift
else:
if self.model_mode is ModelMode.MULTIPLICATIVE:
self.model_mode = ModelMode.ADDITIVE
logger.warning("Negative Theta line. Fallback to additive model")
theta_t = self.theta * ts_values + (1 - self.theta) * theta0_in
self.model = hw.SimpleExpSmoothing(theta_t).fit()
theta2_in = self.model.fittedvalues
self.fitted_values = self.wses * theta2_in + self.wdrift * theta0_in
if self.is_seasonal:
if self.season_mode is SeasonalityMode.ADDITIVE:
self.fitted_values += self.seasonality.univariate_values(copy=False)
elif self.season_mode is SeasonalityMode.MULTIPLICATIVE:
self.fitted_values *= self.seasonality.univariate_values(copy=False)
# Fitted values are the results of the fit of the model on the train series. A good fit of the model
# will lead to fitted_values similar to ts. But one cannot see if it overfits.
if self.normalization:
self.fitted_values *= self.mean
return self
for cat in data_categories[::-1]:
# Load TimeSeries from M4
ts_train = pkl.load(open("dataset/train_" + cat + ".pkl", "rb"))
ts_test = pkl.load(open("dataset/test_" + cat + ".pkl", "rb"))
# Test models on all time series
mase_all = []
smape_all = []
m = int(info_dataset.Frequency[cat[0] + "1"])
for train, test in _build_tqdm_iterator(zip(ts_train, ts_test),
verbose=True):
train_des = train
seasonOut = 1
if m > 1:
if check_seasonality(train, m=m, max_lag=2 * m):
_, season = extract_trend_and_seasonality(
train, m, model=ModelMode.MULTIPLICATIVE)
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)]
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)