def test_avg_mean(forecasters):
"""Assert `mean` aggfunc returns the same values as `average` with equal weights."""
y = make_forecasting_problem()
forecaster = EnsembleForecaster(forecasters)
forecaster.fit(y, fh=[1, 2, 3])
mean_pred = forecaster.predict()
forecaster_1 = EnsembleForecaster(forecasters, aggfunc="mean", weights=[1, 1])
forecaster_1.fit(y, fh=[1, 2, 3])
avg_pred = forecaster_1.predict()
pd.testing.assert_series_equal(mean_pred, avg_pred)
def test_aggregation_unweighted(forecasters, aggfunc):
"""Assert aggfunc returns the correct values."""
y = make_forecasting_problem()
forecaster = EnsembleForecaster(forecasters=forecasters, aggfunc=aggfunc)
forecaster.fit(y, fh=[1, 2, 3])
actual_pred = forecaster.predict()
predictions = []
_aggfunc = VALID_AGG_FUNCS[aggfunc]["unweighted"]
for _, forecaster in forecasters:
f = forecaster
f.fit(y)
f_pred = f.predict(fh=[1, 2, 3])
predictions.append(f_pred)
predictions = pd.DataFrame(predictions).T
expected_pred = predictions.apply(func=_aggfunc, axis=1)
pd.testing.assert_series_equal(actual_pred, expected_pred)
def run_sktimes(dept_id, store_id):
# create timeseries for fbprophet
ts = CreateTimeSeries(dept_id, store_id)
# sktime ensembler
forecaster = EnsembleForecaster([
('naive_ses', NaiveForecaster(sp=28, strategy="seasonal_last")),
('naive', NaiveForecaster(strategy="last")),
('theta_ses', ThetaForecaster(sp=28)), ('theta', ThetaForecaster()),
("exp_ses", ExponentialSmoothing(seasonal="additive", sp=28)),
("exp_damped",
ExponentialSmoothing(trend='additive',
damped=True,
seasonal="additive",
sp=28))
])
forecaster.fit(ts.y + 1)
y_pred = forecaster.predict(np.arange(1, 29))
return np.append(np.array([dept_id, store_id]), y_pred - 1)
def construct_M4_forecasters(sp, fh):
kwargs = {"model": SEASONAL_MODEL, "sp": sp} if sp > 1 else {}
theta_bc = make_pipeline(
ConditionalDeseasonalizer(seasonality_test=seasonality_test_R,
**kwargs), BoxCoxTransformer(bounds=(0, 1)),
ThetaForecaster(deseasonalise=False))
"""
MLP = make_pipeline(
ConditionalDeseasonalizer(seasonality_test=seasonality_test_Python,
**kwargs),
Detrender(PolynomialTrendForecaster(degree=1, with_intercept=True)),
RecursiveRegressionForecaster(
regressor=MLPRegressor(hidden_layer_sizes=6, activation="identity",
solver="adam", max_iter=100,
learning_rate="adaptive",
learning_rate_init=0.001),
window_length=3)
)
RNN = make_pipeline(
ConditionalDeseasonalizer(seasonality_test=seasonality_test_Python,
**kwargs),
Detrender(PolynomialTrendForecaster(degree=1, with_intercept=True)),
RecursiveTimeSeriesRegressionForecaster(
regressor=SimpleRNNRegressor(nb_epochs=100),
window_length=3)
)
"""
forecasters = {
"Naive":
NaiveForecaster(strategy="last"),
"sNaive":
NaiveForecaster(strategy="seasonal_last", sp=sp),
"Naive2":
deseasonalise(NaiveForecaster(strategy="last"), **kwargs),
"SES":
deseasonalise(ses, **kwargs),
"Holt":
deseasonalise(holt, **kwargs),
"Damped":
deseasonalise(damped, **kwargs),
"Theta":
deseasonalise(ThetaForecaster(deseasonalise=False), **kwargs),
"ARIMA":
AutoARIMA(suppress_warnings=True, error_action="ignore", sp=sp),
"Com":
deseasonalise(
EnsembleForecaster([("ses", ses), ("holt", holt),
("damped", damped)]), **kwargs),
# "MLP": MLP,
# "RNN": RNN,
"260":
theta_bc,
}
return forecasters
def test_invalid_aggfuncs(forecasters, aggfunc):
"""Check if invalid aggregation functions return Error."""
y = make_forecasting_problem()
forecaster = EnsembleForecaster(forecasters=forecasters, aggfunc=aggfunc)
forecaster.fit(y, fh=[1, 2])
with pytest.raises(ValueError, match=r"not recognized"):
forecaster.predict()
def test_aggregation_weighted(forecasters, aggfunc, weights):
"""Assert weighted aggfunc returns the correct values."""
y = make_forecasting_problem()
forecaster = EnsembleForecaster(
forecasters=forecasters, aggfunc=aggfunc, weights=weights
)
forecaster.fit(y, fh=[1, 2, 3])
actual_pred = forecaster.predict()
predictions = []
for _, forecaster in forecasters:
f = forecaster
f.fit(y)
f_pred = f.predict(fh=[1, 2, 3])
predictions.append(f_pred)
predictions = pd.DataFrame(predictions).T
_aggfunc = VALID_AGG_FUNCS[aggfunc]["weighted"]
expected_pred = pd.Series(
_aggfunc(predictions, axis=1, weights=np.array(weights)),
index=predictions.index,
)
# expected_pred = predictions.apply(func=_aggfunc, axis=1, weights=weights)
pd.testing.assert_series_equal(actual_pred, expected_pred)
def test_aggregation_weighted(forecasters, aggfunc, weights):
"""Assert weighted aggfunc returns the correct values."""
y = make_forecasting_problem()
forecaster = EnsembleForecaster(forecasters=forecasters,
aggfunc=aggfunc,
weights=weights)
forecaster.fit(y, fh=[1, 2, 3])
actual_pred = forecaster.predict()
predictions = []
for _, forecaster in forecasters:
f = forecaster
f.fit(y)
f_pred = f.predict(fh=[1, 2, 3])
predictions.append(f_pred)
predictions = pd.DataFrame(predictions)
if aggfunc == "mean":
func = np.average
else:
func = gmean
expected_pred = predictions.apply(func=func, axis=0, weights=weights)
pd.testing.assert_series_equal(actual_pred, expected_pred)
def main():
df = datasets.load_airline(
) #Univariate, monthly records from 1949 to 60 (144 records)
y_train, y_test = temporal_train_test_split(
df, test_size=36) #36 months for testing
forecaster = NaiveForecaster(
strategy='seasonal_last', sp=12
) #model strategy: last, mean, seasonal_last. sp=12months (yearly season)
forecaster.fit(y_train) #fit
fh = np.arange(1,
len(y_test) +
1) #forecast horizon: array with the same lenght of y_test
y_pred = forecaster.predict(fh) #pred
forecaster2 = AutoARIMA(sp=12, suppress_warnings=True, trace=1)
forecaster2.fit(y_train)
y_pred2 = forecaster2.predict(fh)
forecaster3 = ExponentialSmoothing(trend='add',
damped='True',
seasonal='multiplicative',
sp=12)
forecaster3.fit(y_train)
y_pred3 = forecaster3.predict(fh)
forecaster4 = ThetaForecaster(sp=12)
forecaster4.fit(y_train)
y_pred4 = forecaster4.predict(fh)
forecaster5 = EnsembleForecaster([
('NaiveForecaster', NaiveForecaster(strategy='seasonal_last', sp=12)),
('AutoARIMA', AutoARIMA(sp=12, suppress_warnings=True)),
('Exp Smoothing',
ExponentialSmoothing(trend='add',
damped='True',
seasonal='multiplicative',
sp=12)), ('Theta', ThetaForecaster(sp=12))
])
forecaster5.fit(y_train)
y_pred5 = forecaster5.predict(fh)
plot_ys(y_train,
y_test,
y_pred,
y_pred2,
y_pred3,
y_pred4,
y_pred5,
labels=[
'Train', 'Test', 'Naive Forecaster', 'AutoARIMA',
'Exp Smoothing', 'Theta', 'Ensemble'
])
plt.xlabel('Months')
plt.ylabel('Number of flights')
plt.title(
'Time series of the number of international flights in function of time'
)
plt.show()
print('SMAPE Error for NaiveForecaster is:',
100 * round(smape_loss(y_test, y_pred), 3), '%')
print('SMAPE Error for AutoARIMA is:',
100 * round(smape_loss(y_test, y_pred2), 3), '%')
print('SMAPE Error for Exp Smoothing is:',
100 * round(smape_loss(y_test, y_pred3), 3), '%')
print('SMAPE Error for Theta is:',
100 * round(smape_loss(y_test, y_pred4), 3), '%')
print('SMAPE Error for Ensemble is:',
100 * round(smape_loss(y_test, y_pred5), 3), '%')
def forecast(data,
customer_id,
start='2017-01',
end='2019-04',
model_type='NaiveForecaster',
test_size_month=5,
model_storage_path=''):
"""
Main function for build forecasting model on selected customer and time interval, save the model and plotting
Parameters
----------
data: pandas DataFrame
main dataset with customer_id, product_id and Timestamp
customer_id: int
start: string
start year and month in '2020-01' format
end: string
end year and month in '2020-01' format *** this month will not be included ***
model_type:
type of model to use in forecasting
select from : ['NaiveForecaster', 'PolynomialTrendForecaster', 'ThetaForecaster', 'KNeighborsRegressor',
'ExponentialSmoothing', 'AutoETS', 'AutoARIMA', 'TBATS', 'BATS', 'EnsembleForecaster']
test_size_month:
number of month that will be excluded from end of interval to use as test dataset
model_storage_path: string
the folder that you want to store saved models
Returns
-------
sMAPE Loss: print
plot: matplotlib figure
plot train, test and predicted values
"""
y_train, y_test = temporal_train_test_split(prepare_data(data,
customer_id,
start=start,
end=end),
test_size=test_size_month)
fh = ForecastingHorizon(y_test.index, is_relative=False)
if model_type == 'NaiveForecaster':
forecaster = NaiveForecaster(strategy="last", sp=12)
elif model_type == 'PolynomialTrendForecaster':
forecaster = PolynomialTrendForecaster(degree=2)
elif model_type == 'ThetaForecaster':
forecaster = ThetaForecaster(sp=6)
elif model_type == 'KNeighborsRegressor':
regressor = KNeighborsRegressor(n_neighbors=1)
forecaster = ReducedRegressionForecaster(regressor=regressor,
window_length=12,
strategy="recursive")
elif model_type == 'ExponentialSmoothing':
forecaster = ExponentialSmoothing(trend="add",
seasonal="multiplicative",
sp=12)
elif model_type == 'AutoETS':
forecaster = AutoETS(auto=True, sp=12, n_jobs=-1)
elif model_type == 'AutoARIMA':
forecaster = AutoARIMA(sp=12, suppress_warnings=True)
elif model_type == 'TBATS':
forecaster = TBATS(sp=12, use_trend=True, use_box_cox=False)
elif model_type == 'BATS':
forecaster = BATS(sp=12, use_trend=True, use_box_cox=False)
elif model_type == 'EnsembleForecaster':
forecaster = EnsembleForecaster([
("ses", ExponentialSmoothing(seasonal="multiplicative", sp=12)),
(
"holt",
ExponentialSmoothing(trend="add",
damped_trend=False,
seasonal="multiplicative",
sp=12),
),
(
"damped",
ExponentialSmoothing(trend="add",
damped_trend=True,
seasonal="multiplicative",
sp=12),
),
])
try:
forecaster.fit(y_train)
except:
forecaster.fit(y_train + 1)
y_pred = forecaster.predict(fh)
dump(
forecaster,
f'{model_storage_path}/{customer_id}_{model_type}_{start}_{end}_{test_size_month}.model'
)
print('sMAPE Loss :', smape_loss(y_pred, y_test))
plot = plot_series(y_train,
y_test,
y_pred,
labels=["y_train", "y_test", "y_pred"])
return plot
def genforecast(data):
from sktime.forecasting.model_selection import temporal_train_test_split
import numpy as np
import math
y_train, y_test = temporal_train_test_split(data)
fh = np.arange(1, len(y_test) + 1)
testct = len(y_test)
from sktime.forecasting.naive import NaiveForecaster
forecaster = NaiveForecaster(strategy="drift")
forecaster.fit(y_train)
y_pred_naive = forecaster.predict(fh)
from sktime.performance_metrics.forecasting import smape_loss
naive_acc = round(smape_loss(y_pred_naive, y_test), 4)
#full model dev and forecast next 5 days
forecaster.fit(data)
futurewin = np.arange(1, 6) # 5 day in future prediction
fut_pred = forecaster.predict(futurewin)
min_naive = round(min(fut_pred), 2)
max_naive = round(max(fut_pred), 2)
from sktime.forecasting.trend import PolynomialTrendForecaster
forecaster = PolynomialTrendForecaster(degree=1)
forecaster.fit(y_train)
y_pred_poly = forecaster.predict(fh)
from sktime.performance_metrics.forecasting import smape_loss
poly_acc = round(smape_loss(y_pred_poly, y_test), 4)
#full model dev and forecast next 5 days
forecaster.fit(data)
futurewin = np.arange(1, 6) # 5 day in future prediction
fut_pred = forecaster.predict(futurewin)
min_poly = round(min(fut_pred), 2)
max_poly = round(max(fut_pred), 2)
from sktime.forecasting.compose import EnsembleForecaster
from sktime.forecasting.exp_smoothing import ExponentialSmoothing
sp1 = math.floor(len(y_test) / 4)
sp2 = min(sp1, 12)
spval = max(2, sp2)
forecaster = EnsembleForecaster([
("ses", ExponentialSmoothing(seasonal="multiplicative", sp=spval)),
("holt",
ExponentialSmoothing(trend="add",
damped=False,
seasonal="multiplicative",
sp=spval)),
("damped",
ExponentialSmoothing(trend="add",
damped=True,
seasonal="multiplicative",
sp=spval))
])
forecaster.fit(y_train)
y_pred_ensem = forecaster.predict(fh)
ensem_acc = round(smape_loss(y_test, y_pred_ensem), 4)
#full model dev and forecast next 5 days
forecaster.fit(data)
futurewin = np.arange(1, 6) # 5 day in future prediction
fut_pred = forecaster.predict(futurewin)
min_ensem = round(min(fut_pred), 2)
max_ensem = round(max(fut_pred), 2)
from sklearn.neighbors import KNeighborsRegressor
regressor = KNeighborsRegressor(n_neighbors=1)
from sktime.forecasting.compose import ReducedRegressionForecaster
forecaster = ReducedRegressionForecaster(regressor=regressor,
window_length=15,
strategy="recursive")
param_grid = {"window_length": [5, 10, 15]}
from sktime.forecasting.model_selection import SlidingWindowSplitter
from sktime.forecasting.model_selection import ForecastingGridSearchCV
# we fit the forecaster on the initial window, and then use temporal cross-validation to find the optimal parameter
cv = SlidingWindowSplitter(initial_window=int(len(y_train) * 0.5))
gscv = ForecastingGridSearchCV(forecaster, cv=cv, param_grid=param_grid)
gscv.fit(y_train)
y_pred_redreg = gscv.predict(fh)
redreg_acc = round(smape_loss(y_test, y_pred_redreg), 4)
#full model dev and forecast next 5 days
gscv.fit(data)
futurewin = np.arange(1, 6) # 5 day in future prediction
fut_pred = gscv.predict(futurewin)
min_redreg = round(min(fut_pred), 2)
max_redreg = round(max(fut_pred), 2)
return min_naive, max_naive, min_poly, max_poly, min_ensem, max_ensem, min_redreg, max_redreg, y_test, testct, y_pred_naive, naive_acc, y_pred_poly, poly_acc, y_pred_ensem, ensem_acc, y_pred_redreg, redreg_acc
sktime提供了用于组合模型构建的模块化API，以进行预测。
* Ensembling
像scikit-learn一样，sktime提供了一个元预测器来集成多种预测算法。
例如，我们可以如下组合指数平滑的不同变体：
''')
from sktime.forecasting.compose import EnsembleForecaster
forecaster = EnsembleForecaster([
("ses", ExponentialSmoothing(seasonal="multiplicative", sp=12)),
("holt",
ExponentialSmoothing(trend="add",
damped=False,
seasonal="multiplicative",
sp=12)),
("damped",
ExponentialSmoothing(trend="add",
damped=True,
seasonal="multiplicative",
sp=12))
])
forecaster.fit(y_train)
y_pred = forecaster.predict(fh)
plot_ys(y_train, y_test, y_pred, labels=["y_train", "y_test", "y_pred"])
st.pyplot()
st.write("smape_loss(y_test, y_pred):", smape_loss(y_test, y_pred))
st.write('''
* Tuning
In the `ReducedRegressionForecaster`,