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()
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()
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()
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()
