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()