def optimize(request):
"""
Performs Holt Winters Parameter Optimization on the given post data.
Expects the following values set in the post of the request:
seasonLength - integer
valuesToForecast - integer
data - two dimensional array of [timestamp, value]
"""
#Parse arguments
seasonLength = int(request.POST.get('seasonLength', 6))
valuesToForecast = int(request.POST.get('valuesToForecast', 0))
data = json.loads(request.POST.get('data', []))
original = TimeSeries.from_twodim_list(data)
original.normalize("day") #due to bug in TimeSeries.apply
original.set_timeformat("%d.%m")
#optimize smoothing
hwm = HoltWintersMethod(seasonLength = seasonLength, valuesToForecast = valuesToForecast)
gridSearch = GridSearch(SMAPE)
optimal_forecasting, error, optimal_params = gridSearch.optimize(original, [hwm])
#perform smoothing
smoothed = optimal_forecasting.execute(original)
smoothed.set_timeformat("%d.%m")
result = { 'params': optimal_params,
'original': original,
'smoothed': smoothed,
'error': round(error.get_error(), 3)
}
return Response(json.dumps(result, cls=PycastEncoder), content_type='application/json')
def optimize(request):
"""
Performs Holt Winters Parameter Optimization on the given post data.
Expects the following values set in the post of the request:
seasonLength - integer
valuesToForecast - integer
data - two dimensional array of [timestamp, value]
"""
#Parse arguments
seasonLength = int(request.POST.get('seasonLength', 6))
valuesToForecast = int(request.POST.get('valuesToForecast', 0))
data = json.loads(request.POST.get('data', []))
original = TimeSeries.from_twodim_list(data)
original.normalize("day") #due to bug in TimeSeries.apply
original.set_timeformat("%d.%m")
#optimize smoothing
hwm = HoltWintersMethod(seasonLength = seasonLength, valuesToForecast = valuesToForecast)
gridSearch = GridSearch(SMAPE)
optimal_forecasting, error, optimal_params = gridSearch.optimize(original, [hwm])
#perform smoothing
smoothed = optimal_forecasting.execute(original)
smoothed.set_timeformat("%d.%m")
result = { 'params': optimal_params,
'original': original,
'smoothed': smoothed,
'error': round(error.get_error(), 3)
}
return itty.Response(json.dumps(result, cls=PycastEncoder), content_type='application/json')
def train_target(self, data_list, model_list):# data_list: [date, value]
orig = TimeSeries(isNormalized=True)
for i in range(len(data_list)):
orig.add_entry(data_list[i][0], data_list[i][1])
gridSearch = GridSearch(SMAPE)
optimal_forecasting, error, optimal_params = gridSearch.optimize(orig, model_list)
#print "======" + str(optimal_forecasting._parameters)
return optimal_forecasting
def outer_optimization_result_test(self):
"""Test the multiple method optimization."""
fm1 = ExponentialSmoothing()
fm2 = HoltMethod()
self.timeSeries.normalize("second")
## automatically determine the best alpha using GridSearch
gridSearch = GridSearch(SMAPE, precision=-2)
result = gridSearch.optimize(self.timeSeries, [fm1, fm2])
from pycast.common.timeseries import TimeSeries
from pycast.methods import HoltWintersMethod
from pycast.optimization import GridSearch
from pycast.errors import SymmetricMeanAbsolutePercentageError as SMAPE
data_list = [1.1, 2.1, 3.3, 4.4, 5.5, 1.1, 2.2, 3.3]
orig = TimeSeries(isNormalized=True)
for i in range(len(data_list)):
orig.add_entry(i, data_list[i]) #offset to first data entry in line
seasonLength = int(5)
season_values = []
for i in range(seasonLength):
season_values.append(float(1.0))
#print season_values
hwm = HoltWintersMethod(seasonLength = seasonLength, valuesToForecast = 3)
hwm.set_parameter("seasonValues", season_values)
gridSearch = GridSearch(SMAPE)
optimal_forecasting, error, optimal_params = gridSearch.optimize(orig, [hwm])
predicted = optimal_forecasting.execute(orig)
print str(orig)
print str(predicted)
#Season indices are given in season file
season_indices_tuple = season_indices.readline().split(',')
seasonLength = int(season_indices_tuple[1])
season_values = []
for i in range(seasonLength):
season_values.append(float(season_indices_tuple[i + 2]))
#print season_values
hwm = HoltWintersMethod(seasonLength=seasonLength,
valuesToForecast=number_forecast)
hwm.set_parameter("seasonValues", season_values)
#Optimize parameters
gridSearch = GridSearch(SMAPE)
optimal_forecasting, error, optimal_params = gridSearch.optimize(
orig, [hwm])
predicted = optimal_forecasting.execute(orig)
#Now add forecasted values to original and calculate error
orig += forecast_check
assert len(orig) == len(
predicted
), "Prediction and original season should have the same length."
total_error = SMAPE()
total_error.initialize(orig, predicted)
forecast_error = SMAPE()
forecast_error.initialize(
forecast_check,
TimeSeries.from_twodim_list(predicted[-len(forecast_check):]))
