def season_length_test(self):
"""Test that the season length has to be greater than 0."""
for seasonLength in xrange(-4, 1):
try:
HoltWintersMethod(seasonLength=seasonLength)
except ValueError:
pass
else:
assert False # pragma: no cover
for seasonLength in xrange(1, 12414, 412):
HoltWintersMethod(seasonLength=seasonLength)
def initialization_test(self):
"""Test the initialization of the HoltWintersMethod method."""
HoltWintersMethod(0.2, 0.3, 0.4, 5)
for alpha in [-0.1, 0.81, 1.1]:
for beta in [-1.4, 0.12, 3.2]:
for gamma in [-0.05, 1.3]:
try:
HoltWintersMethod(alpha, beta, gamma)
except ValueError:
pass
else:
assert False # pragma: no cover
def forecasting_test(self):
data = [
362.0, 385.0, 432.0, 341.0, 382.0, 409.0, 498.0, 387.0, 473.0,
513.0, 582.0, 474.0, 544.0, 582.0, 681.0, 557.0, 628.0, 707.0,
773.0, 592.0, 627.0, 725.0, 854.0, 661.0
]
tsSrc = TimeSeries.from_twodim_list(zip(range(len(data)), data))
expected = [[0.0, 362.0], [1.0, 379.93673257607463],
[2.0, 376.86173719924875], [3.0, 376.0203652542205],
[4.0, 408.21988583215574], [5.0, 407.16235446485433],
[6.0, 430.0950666716297], [7.0, 429.89797609228435],
[8.0, 489.4888959723074], [9.0, 507.8407281475308],
[10.0, 506.3556647249702], [11.0, 523.9422448655133],
[12.0, 556.0311543025242], [13.0, 573.6520991970604],
[14.0, 590.2149136780341], [15.0, 611.8813425659495],
[16.0, 637.0393967524727], [17.0, 684.6600411792656],
[18.0, 675.9589298142507], [19.0, 659.0266828674846],
[20.0, 644.0903317144154], [21.0, 690.4507762388047],
[22.0, 735.3219292023371], [23.0, 737.9752345691215],
[24.0, 669.767091965978], [25.0, 737.5272444120604],
[26.0, 805.3947787747426], [27.0, 902.1522777060334]]
hwm = HoltWintersMethod(.7556, 0.0000001, .9837, 4, valuesToForecast=4)
res = tsSrc.apply(hwm)
#print res
assert len(res) == len(tsSrc) + 4
assert res == TimeSeries.from_twodim_list(expected)
def smoothing_test(self):
""" Test if the smoothing works correctly"""
data = [
362.0, 385.0, 432.0, 341.0, 382.0, 409.0, 498.0, 387.0, 473.0,
513.0, 582.0, 474.0, 544.0, 582.0, 681.0, 557.0, 628.0, 707.0,
773.0, 592.0, 627.0, 725.0, 854.0, 661.0
]
tsSrc = TimeSeries.from_twodim_list(zip(range(len(data)), data))
expected = [[0.0, 362.0], [1.0, 379.93673257607463],
[2.0, 376.86173719924875], [3.0, 376.0203652542205],
[4.0, 408.21988583215574], [5.0, 407.16235446485433],
[6.0, 430.0950666716297], [7.0, 429.89797609228435],
[8.0, 489.4888959723074], [9.0, 507.8407281475308],
[10.0, 506.3556647249702], [11.0, 523.9422448655133],
[12.0, 556.0311543025242], [13.0, 573.6520991970604],
[14.0, 590.2149136780341], [15.0, 611.8813425659495],
[16.0, 637.0393967524727], [17.0, 684.6600411792656],
[18.0, 675.9589298142507], [19.0, 659.0266828674846],
[20.0, 644.0903317144154], [21.0, 690.4507762388047],
[22.0, 735.3219292023371], [23.0, 737.9752345691215]]
hwm = HoltWintersMethod(.7556, 0.0000001, .9837, 4, valuesToForecast=0)
initialA_2 = hwm.computeA(2, tsSrc)
assert initialA_2 == 510.5, "Third initial A_2 should be 510.5, but it %d" % initialA_2
initialTrend = hwm.initialTrendSmoothingFactors(tsSrc)
assert initialTrend == 9.75, "Initial Trend should be 9.75 but is %d" % initialTrend
#correctness is not proven, but will be enough for regression testing
resTS = tsSrc.apply(hwm)
expectedTS = TimeSeries.from_twodim_list(expected)
assert len(resTS) == len(expectedTS)
assert resTS == expectedTS, "Smoothing result not correct."
def initial_trend_values_test(self):
hwm = HoltWintersMethod(seasonLength=4)
data = [[0, 362.0], [1, 385.0], [2, 432.0], [3, 341.0], [4, 382.0],
[5, 425.0]]
tsSrc = TimeSeries.from_twodim_list(data)
trend = hwm.initialTrendSmoothingFactors(tsSrc)
assert trend == 7.5, "Initial Trend should be 7.5 but is %f" % trend
def sanity_test(self):
"""HoltWinters should throw an Exception if applied to a Time Series shorter than the season length"""
hwm = HoltWintersMethod(seasonLength=2)
data = [[0.0, 152]]
tsSrc = TimeSeries.from_twodim_list(data)
try:
tsSrc.apply(hwm)
except ValueError, e:
pass
def season_factor_initialization_test(self):
""" Test if seasonal correction factors are initialized correctly."""
hwm = HoltWintersMethod(seasonLength=4)
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)
#correctness is not proven, but will be enough for regression testing
assert seasonValues == [
0.9302895649920525, 0.9980629019785198, 1.1551483413078523,
0.9164991917215755
], "Season Values are not initialized correctly" # 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
forecast_check = TimeSeries(isNormalized=True)
for j in range(number_obeservations,
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()