def preset_season_factor_test(self):
"""Initial Season Factors should be presetable"""
hwm = HoltWintersMethod(seasonLength=4)
factors = [0,1,2,3]
hwm.set_parameter("seasonValues", factors)
data = [[0, 362.0], [1,385.0], [2, 432.0], [3, 341.0], [4, 382.0], [5, 409.0], [6, 498.0], [7, 387.0], [8, 473.0], [9, 513.0], [10, 582.0], [11, 474.0]]
tsSrc = TimeSeries.from_twodim_list(data)
seasonValues = hwm.initSeasonFactors(tsSrc)
assert seasonValues == factors, "Preset Season Factors are not returned by initSeasonFactors"
hwm.set_parameter("seasonValues", factors[:2])
try:
hwm.initSeasonFactors(tsSrc)
except AssertionError:
pass
else:
assert False, "If preset season factors and season length do not comply, initSeasonFactors should throw an AssertionError" # pragma: no cover
def preset_season_factor_test(self):
"""Initial Season Factors should be presetable"""
hwm = HoltWintersMethod(seasonLength=4)
factors = [0, 1, 2, 3]
hwm.set_parameter("seasonValues", factors)
data = [[0, 362.0], [1, 385.0], [2, 432.0], [3, 341.0], [4, 382.0],
[5, 409.0], [6, 498.0], [7, 387.0], [8, 473.0], [9, 513.0],
[10, 582.0], [11, 474.0]]
tsSrc = TimeSeries.from_twodim_list(data)
seasonValues = hwm.initSeasonFactors(tsSrc)
assert seasonValues == factors, "Preset Season Factors are not returned by initSeasonFactors"
hwm.set_parameter("seasonValues", factors[:2])
try:
hwm.initSeasonFactors(tsSrc)
except AssertionError:
pass
else:
assert False, "If preset season factors and season length do not comply, initSeasonFactors should throw an AssertionError" # pragma: no cover
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)
number_obeservations + number_forecast):
forecast_check.add_entry(j, float(series_tuples[j + 7]))
#print forecast_check
#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)