def one_forecast(self, trainingdata, testdata, start_forecast,
end_forecast, period_errors):
#electrical_forecast = DayTypeForecast(BaseEnvironment(start_forecast, False, False), trainingdata, samples_per_hour=1)
#forecast = [electrical_forecast.get_forecast_at(timestamp) for timestamp in range(start_forecast,end_forecast,3600)]
forecast_nodaysplit, (alpha, beta, gamma, delta,
autocorr), insample = double_seasonal(
trainingdata,
24,
24 * 7,
len(testdata),
optimization_type="RMSE")
for day, length in enumerate(range(24, 14 * 24, 24)):
period_errors[day][0] += self.rmse(forecast_nodaysplit[:length],
testdata[:length])
period_errors[day][1] += StatisticalForecast.MASE(
trainingdata, forecast_nodaysplit[:length], testdata[:length])
def strom_real(self):
sep = os.path.sep
t0 = time()
path = os.path.join(
BASE_DIR, "server" + sep + "forecasting" + sep + "devices" + sep +
"data" + sep + "Electricity_2012Neuendorf.csv")
raw_dataset1 = DataLoader.load_from_file(
path, "Strom - Verbrauchertotal (Aktuell)", "\t")
dates1 = DataLoader.load_from_file(path, "Datum", "\t")
path2 = os.path.join(
BASE_DIR, "server" + sep + "forecasting" + sep + "devices" + sep +
"data" + sep + "Electricity_2013.csv")
raw_dataset2 = DataLoader.load_from_file(
path2, "Strom - Verbrauchertotal (Aktuell)", "\t")
dates2 = DataLoader.load_from_file(path2, "Datum", "\t")
t1 = time()
dates1 = StatisticalForecast.make_hourly([int(d) for d in dates1], 6)
dates2 = StatisticalForecast.make_hourly([int(d) for d in dates2], 6)
demand1 = StatisticalForecast.make_hourly(
[float(val) / 1000.0 for val in raw_dataset1], 6)
demand2 = StatisticalForecast.make_hourly(
[float(val) / 1000.0 for val in raw_dataset2], 6)
t2 = time()
rm = StatisticalForecast.MASE(demand1, demand1[:len(demand2)], demand2)
#split_testdata1 = DayTypeForecast.split_weekdata(demand1,samples_per_hour=1,start_date=datetime.fromtimestamp(dates1[0]))
#split_testdata2 = DayTypeForecast.split_weekdata(demand2,samples_per_hour=1,start_date=datetime.fromtimestamp(dates2[0]))
#for index, dataset in enumerate(split_testdata1):
# print self.rmse(split_testdata2[index], dataset)#StatisticalForecast.MASE(dataset, dataset[:len(split_testdata2[index])],split_testdata2[index][:len(dataset)])
t3 = time()
print "t0 ", t1 - t0, "t1 ", t2 - t1, "t3 ", t3 - t2
print rm
