def forecast_demands(self,verbose=False):
""" See :meth:`StatisticalForecast.forecast_demands`."""
logger.debug("forecasting demands with double seasonal HW..")
cached = StatisticalForecast.read_from_cache(self)
if cached != None:
return cached
demand = self.demands[0] #dshw demands only contains one dataset
sph = self.samples_per_hour
fc = self.output_weeks * 24 * 7 * self.samples_per_hour #forecast_length
(alpha, beta, gamma, delta, autocorr) = (None for i in range(5))
t = time.time()
forecast_values, (alpha, beta, gamma, delta, autocorrelation),in_sample = double_seasonal(demand, m=int(24*sph), m2=int(24*7*sph),
forecast=int(fc), alpha=alpha, beta=beta, gamma=gamma, delta=delta,
autocorrelation=autocorr)
if verbose:
mse = ((in_sample - forecast_values) ** 2).mean(axis=None)
calculated_parameters = {
"alpha": alpha, "beta": beta, "gamma": gamma, "delta":delta,
"autocorrelation":autocorrelation, "mse": mse}
print "use auto HW ",calculated_parameters
logger.debug("doubleseasonal completed in:" + str(time.time()-t) + " s")
return forecast_values
def update_hw(val):
alpha = alpha_slider.val
autocorr = autocorr_slider.val
beta = 0.0
gamma = gamma_slider.val
delta = delta_slider.val
forecast_values, params, insample = double_seasonal(trainingdata, m,m2,fc, alpha, beta, gamma,delta,autocorr)
values ={ 'forecasting':forecast_values, 'measured':testdata}
forecast_plot.set_ydata(forecast_values)
sim_plot.set_ydata(testdata)
fig.canvas.draw_idle()
print alpha, autocorr, gamma, MSE(testdata, forecast_values)
def forecast_demands(self, verbose=False):
""" See :meth:`StatisticalForecast.forecast_demands`."""
logger.debug("forecasting demands with double seasonal HW..")
cached = StatisticalForecast.read_from_cache(self)
if cached != None:
return cached
demand = self.demands[0] #dshw demands only contains one dataset
sph = self.samples_per_hour
fc = self.output_weeks * 24 * 7 * self.samples_per_hour #forecast_length
(alpha, beta, gamma, delta, autocorr) = (None for i in range(5))
t = time.time()
forecast_values, (alpha, beta, gamma, delta,
autocorrelation), in_sample = double_seasonal(
demand,
m=int(24 * sph),
m2=int(24 * 7 * sph),
forecast=int(fc),
alpha=alpha,
beta=beta,
gamma=gamma,
delta=delta,
autocorrelation=autocorr)
if verbose:
mse = ((in_sample - forecast_values)**2).mean(axis=None)
calculated_parameters = {
"alpha": alpha,
"beta": beta,
"gamma": gamma,
"delta": delta,
"autocorrelation": autocorrelation,
"mse": mse
}
print "use auto HW ", calculated_parameters
logger.debug("doubleseasonal completed in:" + str(time.time() - t) +
" s")
return forecast_values
def value_changer():
try:
from matplotlib.widgets import Slider, Button, RadioButtons
from pylab import axes
except:
print "ljdlj"
sep = os.path.sep
path = os.path.join(BASE_DIR, "server" + sep + "forecasting" + sep + "simulation" + sep + "demodata" + sep + "demo_electricity_2014.csv")
raw_data = DataLoader.load_from_file(path, "Strom - Verbrauchertotal (Aktuell)",delim="\t")
ind = len(raw_data) / 2
kW_data = StatisticalForecast.make_hourly([float(val) / 1000.0 for val in raw_data],6) #cast to float and convert to kW
dates = [int(d) for d in DataLoader.load_from_file(path, "Datum", "\t")]
input = make_hourly(kW_data,6)[-24*7:]
start = calendar.timegm(datetime(year=2014,month=1,day=2).timetuple())
start_index = approximate_index(dates, start)
train_len= 24*7*8
trainingdata = kW_data[start_index:start_index+train_len]
test_start = start_index+train_len
testdata = kW_data[test_start:test_start+7*24*2]
start_forecast = test_start*3600
end_forecast = start_forecast + len(testdata) * 3600
alpha = 0.0000001
beta = 0.0
gamma = 0.05
delta = 0.01
autocorr = 0.01
#plot_dataset(values)
m = 24
m2 = 24 * 7
#forecast length
fc = int(len(testdata))
forecast_values, params, insample = double_seasonal(trainingdata, m,m2,fc, alpha, beta, gamma,delta,autocorr)
values ={ 'forecasting':forecast_values, 'measured':testdata}
(fig, sim_plot,forecast_plot) = plot_dataset(values, 0,block=False)
axcolor = 'lightgoldenrodyellow'
axalpa = axes([0.25, 0.02, 0.65, 0.02], axisbg=axcolor)
axautocorr = axes([0.25, 0.05, 0.65, 0.02], axisbg=axcolor)
axgamma = axes([0.25, 0.08, 0.65, 0.02], axisbg=axcolor)
axdelta = axes([0.25, 0.11, 0.65, 0.02], axisbg=axcolor)
alpha_slider = Slider(axalpa, 'Alpha', 0.0, 1.0, valinit=alpha)
gamma_slider = Slider(axgamma, 'Gamma', 0.0, 1.0, valinit=gamma)
delta_slider = Slider(axdelta, 'Delta', 0.0, 1.0, valinit=delta)
autocorr_slider = Slider(axautocorr, 'autocorr_slider', 0.0, 1.0, valinit=autocorr)
def update_hw(val):
alpha = alpha_slider.val
autocorr = autocorr_slider.val
beta = 0.0
gamma = gamma_slider.val
delta = delta_slider.val
forecast_values, params, insample = double_seasonal(trainingdata, m,m2,fc, alpha, beta, gamma,delta,autocorr)
values ={ 'forecasting':forecast_values, 'measured':testdata}
forecast_plot.set_ydata(forecast_values)
sim_plot.set_ydata(testdata)
fig.canvas.draw_idle()
print alpha, autocorr, gamma, MSE(testdata, forecast_values)
alpha_slider.on_changed(update_hw)
autocorr_slider.on_changed(update_hw)
gamma_slider.on_changed(update_hw)
delta_slider.on_changed(update_hw)
plt.show()
