def test_data_none(self):
w = self.widget
self.send_signal(w.Inputs.time_series, None)
# send selection when no data on the input
self.send_signal(
w.Inputs.features, AttributeList(self.airpassengers.attributes)
)
# send forecast on empty data
model1 = ARIMA((3, 1, 1)).fit(self.airpassengers)
prediction = model1.predict(10, as_table=True)
self.send_signal(w.Inputs.forecast, prediction, 1)
def test_bad_model(self):
"""
Do not fail on a bad model.
GH-37
"""
w = self.widget
table = Table("housing")
time_series = Timeseries(table)
model = ARIMA((2, 5, 1), 0)
self.assertFalse(w.Warning.model_not_appropriate.is_shown())
self.send_signal(w.Inputs.time_series, time_series)
self.send_signal(w.Inputs.time_series_model, model, 0)
w.controls.autocommit.click()
self.assertTrue(w.Warning.model_not_appropriate.is_shown())
self.send_signal(w.Inputs.time_series_model, None, 0)
self.assertFalse(w.Warning.model_not_appropriate.is_shown())
if var.is_continuous and var != data.time_variable
])
@Inputs.forecast
def set_forecast(self, forecast, id):
if forecast is not None:
self.forecasts[id] = forecast
else:
self.forecasts.pop(id, None)
# TODO: update currently shown plots
if __name__ == "__main__":
from AnyQt.QtWidgets import QApplication
from orangecontrib.timeseries import ARIMA, VAR
a = QApplication([])
ow = OWLineChart()
airpassengers = Timeseries('airpassengers')
ow.set_data(airpassengers),
msft = airpassengers.interp()
model = ARIMA((3, 1, 1)).fit(airpassengers)
ow.set_forecast(model.predict(10, as_table=True), 0)
model = VAR(4).fit(msft)
ow.set_forecast(model.predict(10, as_table=True), 1)
ow.show()
a.exec()
def test_model_evaluation(self):
models = [ARIMA((1, 1, 0)), ARIMA((2, 1, 1)), VAR(1), VAR(3)]
results = model_evaluation(data, models, n_folds=10, forecast_steps=3)
results = np.array(results, dtype=object)
self.assertEqual(results.shape, (4 * 2 + 1, 8))
np.testing.assert_equal(results[1:, 1:].astype(float) > 0, True)
def test_predict_as_table(self):
model = ARIMA((2, 1, 0))
model.fit(data)
forecast = model.predict(10, as_table=True)
self.assertEqual(len(forecast.domain.variables), 1 + 2)
def test_predict(self):
model = ARIMA((2, 1, 0))
model.fit(data)
forecast, ci95_low, ci95_high = model.predict(10)
self.assertTrue(
np.logical_and(forecast > ci95_low, forecast < ci95_high).all())
if __name__ == "__main__":
from AnyQt.QtWidgets import QApplication
from Orange.data import Domain
from orangecontrib.timeseries import ARIMA, VAR
a = QApplication([])
ow = OWModelEvaluation()
data = Timeseries('yahoo_MSFT')
# Make Adjusted Close a class variable
attrs = [var.name for var in data.domain.attributes]
if 'Adj Close' in attrs:
attrs.remove('Adj Close')
data = Timeseries(
Domain(attrs, [data.domain['Adj Close']], None,
source=data.domain), data)
ow.set_data(data)
ow.set_model(ARIMA((1, 1, 1)), 1)
ow.set_model(ARIMA((2, 1, 0)), 2)
# ow.set_model(ARIMA((0, 1, 1)), 3)
# ow.set_model(ARIMA((4, 1, 0)), 4)
ow.set_model(VAR(1), 11)
ow.set_model(VAR(5), 12)
# ow.set_model(VAR(6), 14)
ow.show()
a.exec()
super().closeEvent(event)
def closeContext(self) -> None:
"""
Gather configs in contextVariables and close context.
"""
if not self.features:
# only close in case of when features are not present if they are
# feature selection is defined by the input and context should
# not have impact
attrs, is_logit = [], []
for config in self.configs:
attrs.append(config.get_selection())
is_logit.append(config.is_logarithmic)
self.attrs = attrs
self.is_logit = is_logit
super().closeContext()
if __name__ == "__main__":
from orangecontrib.timeseries import ARIMA, VAR
airpassengers = Timeseries.from_file('airpassengers')
msft = airpassengers.interp()
model1 = ARIMA((3, 1, 1)).fit(airpassengers)
model2 = VAR(4).fit(msft)
ow = WidgetPreview(OWLineChart)
ow.run(set_data=airpassengers,
set_forecast=[(model1.predict(10, as_table=True), 0),
(model2.predict(10, as_table=True), 1)])
self.chart.enable_rangeSelector(
isinstance(data.time_variable, TimeVariable))
def set_forecast(self, forecast, id):
if forecast is not None:
self.forecasts[id] = forecast
else:
self.forecasts.pop(id, None)
# TODO: update currently shown plots
if __name__ == "__main__":
from PyQt4.QtGui import QApplication
from orangecontrib.timeseries import ARIMA, VAR
a = QApplication([])
ow = OWLineChart()
msft = Timeseries('yahoo_MSFT')
ow.set_data(msft),
# ow.set_data(Timeseries('UCI-SML2010-1'))
msft = msft.interp()
model = ARIMA((3, 1, 1)).fit(msft)
ow.set_forecast(model.predict(10, as_table=True), 0)
model = VAR(4).fit(msft)
ow.set_forecast(model.predict(10, as_table=True), 1)
ow.show()
a.exec()
def create_learner(self):
return ARIMA((self.p, self.d, self.q), self.use_exog)
self.varmodel.wrap([var for var in data.domain.variables
if var.is_continuous and var != data.time_variable])
@Inputs.forecast
def set_forecast(self, forecast, id):
if forecast is not None:
self.forecasts[id] = forecast
else:
self.forecasts.pop(id, None)
# TODO: update currently shown plots
if __name__ == "__main__":
from AnyQt.QtWidgets import QApplication
from orangecontrib.timeseries import ARIMA, VAR
a = QApplication([])
ow = OWLineChart()
airpassengers = Timeseries('airpassengers')
ow.set_data(airpassengers),
msft = airpassengers.interp()
model = ARIMA((3, 1, 1)).fit(airpassengers)
ow.set_forecast(model.predict(10, as_table=True), 0)
model = VAR(4).fit(msft)
ow.set_forecast(model.predict(10, as_table=True), 1)
ow.show()
a.exec()