- General refactoring to include **kwargs on methods signatures
- Ensemble FTS
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cd2e2b7586
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@ -3,12 +3,11 @@
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import numpy as np
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from statsmodels.tsa.arima_model import ARIMA as stats_arima
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from statsmodels.tsa.arima_model import ARMA
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from pyFTS import fts
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class ARIMA(fts.FTS):
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def __init__(self, name):
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def __init__(self, order, **kwargs):
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super(ARIMA, self).__init__(1, "ARIMA")
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self.name = "ARIMA"
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self.detail = "Auto Regressive Integrated Moving Average"
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@ -23,8 +22,6 @@ class ARIMA(fts.FTS):
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self.minOrder = 1
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def train(self, data, sets, order=1, parameters=None):
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ndata = np.array(self.doTransformations(data))
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if parameters is not None:
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self.p = parameters[0]
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self.d = parameters[1]
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@ -33,44 +30,25 @@ class ARIMA(fts.FTS):
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self.shortname = "ARIMA(" + str(self.p) + "," + str(self.d) + "," + str(self.q) + ")"
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old_fit = self.model_fit
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self.model = stats_arima(ndata, order=(self.p, self.d, self.q))
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#try:
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self.model_fit = self.model.fit(disp=0)
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#except:
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# try:
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# self.model = stats_arima(data, order=(self.p, self.d, self.q))
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# self.model_fit = self.model.fit(disp=1)
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# except:
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# self.model_fit = old_fit
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self.model = stats_arima(data, order=(self.p, self.d, self.q))
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try:
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self.model_fit = self.model.fit(disp=0)
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except:
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try:
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self.model = stats_arima(data, order=(self.p, self.d, self.q))
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self.model_fit = self.model.fit(disp=1)
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except:
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self.model_fit = old_fit
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#self.trained_data = data #.tolist()
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self.trained_data = data #.tolist()
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def ar(self, data):
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return data.dot(self.model_fit.arparams)
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def ma(self, data):
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return data.dot(self.model_fit.maparams)
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def forecast(self, data):
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def forecast(self, data, **kwargs):
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if self.model_fit is None:
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return np.nan
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order = self.p
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ndata = np.array(self.doTransformations(data))
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l = len(ndata)
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ret = []
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ar = np.array([self.ar(ndata[k - self.p: k]) for k in np.arange(self.p, l)])
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residuals = np.array([ar[k - self.p] - ndata[k] for k in np.arange(self.p, l)])
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ma = np.array([self.ma(residuals[k - self.q : k]) for k in np.arange(self.q, len(ar)+1)])
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ret = ar + ma
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ret = self.doInverseTransformations(ret, params=[data[order - 1:]])
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return ret
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for t in data:
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output = self.model_fit.forecast()
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ret.append( output[0] )
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self.trained_data = np.append(self.trained_data, t) #.append(t)
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self.train(self.trained_data,None,order=self.order, parameters=(self.p, self.d, self.q))
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return ret
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@ -773,7 +773,7 @@ def get_distribution_statistics(original, model, steps, resolution):
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ret = list()
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try:
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_s1 = time.time()
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densities1 = model.forecastAheadDistribution(original,steps,resolution, parameters=3)
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densities1 = model.forecastAheadDistribution(original, steps, parameters=3)
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_e1 = time.time()
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ret.append(round(Measures.crps(original, densities1), 3))
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ret.append(round(_e1 - _s1, 3))
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@ -784,7 +784,7 @@ def get_distribution_statistics(original, model, steps, resolution):
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try:
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_s2 = time.time()
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densities2 = model.forecastAheadDistribution(original, steps, resolution, parameters=2)
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densities2 = model.forecastAheadDistribution(original, steps, parameters=2)
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_e2 = time.time()
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ret.append( round(Measures.crps(original, densities2), 3))
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ret.append(round(_e2 - _s2, 3))
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@ -823,8 +823,8 @@ def plotComparedIntervalsAhead(original, models, colors, distributions, time_fro
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for count, fts in enumerate(models, start=0):
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if fts.hasDistributionForecasting and distributions[count]:
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density = fts.forecastAheadDistribution(original[time_from - fts.order:time_from],
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time_to, resolution, parameters=option)
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density = fts.forecastAheadDistribution(original[time_from - fts.order:time_from], time_to,
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parameters=option)
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Y = []
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X = []
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@ -5,13 +5,13 @@ from pyFTS import fts
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class Naive(fts.FTS):
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def __init__(self, name):
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def __init__(self, order, name, **kwargs):
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super(Naive, self).__init__(1, "Naive " + name)
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self.name = "Naïve Model"
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self.detail = "Naïve Model"
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self.benchmark_only = True
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self.isHighOrder = False
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def forecast(self, data):
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def forecast(self, data, **kwargs):
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return [k for k in data]
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@ -8,7 +8,7 @@ from pyFTS import fts
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class QuantileRegression(fts.FTS):
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def __init__(self, name):
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def __init__(self, order, **kwargs):
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super(QuantileRegression, self).__init__(1, "QR")
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self.name = "QR"
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self.detail = "Quantile Regression"
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@ -38,7 +38,7 @@ class QuantileRegression(fts.FTS):
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def linearmodel(self,data,params):
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return params[0] + sum([ data[k] * params[k+1] for k in np.arange(0, self.order) ])
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def forecast(self, data):
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def forecast(self, data, **kwargs):
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ndata = np.array(self.doTransformations(data))
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l = len(ndata)
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@ -53,7 +53,7 @@ class QuantileRegression(fts.FTS):
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return ret
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def forecastInterval(self, data):
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def forecastInterval(self, data, **kwargs):
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ndata = np.array(self.doTransformations(data))
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4
chen.py
4
chen.py
@ -25,7 +25,7 @@ class ConventionalFLRG(object):
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class ConventionalFTS(fts.FTS):
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def __init__(self, name):
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def __init__(self, order, **kwargs):
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super(ConventionalFTS, self).__init__(1, "CFTS " + name)
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self.name = "Conventional FTS"
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self.detail = "Chen"
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@ -48,7 +48,7 @@ class ConventionalFTS(fts.FTS):
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flrs = FLR.generateNonRecurrentFLRs(tmpdata)
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self.flrgs = self.generateFLRG(flrs)
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def forecast(self, data):
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def forecast(self, data, **kwargs):
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ndata = np.array(self.doTransformations(data))
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12
ensemble.py
12
ensemble.py
@ -9,7 +9,7 @@ from pyFTS.common import FLR, FuzzySet, SortedCollection
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from pyFTS import fts
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class EnsembleFTS(fts.FTS):
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def __init__(self, name, update=True):
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def __init__(self, order, name, **kwargs):
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super(EnsembleFTS, self).__init__("Ensemble FTS")
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self.shortname = "Ensemble FTS " + name
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self.name = "Ensemble FTS"
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@ -25,7 +25,7 @@ class EnsembleFTS(fts.FTS):
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pass
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def forecast(self, data):
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def forecast(self, data, **kwargs):
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ndata = np.array(self.doTransformations(data))
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@ -40,7 +40,7 @@ class EnsembleFTS(fts.FTS):
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return ret
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def forecastInterval(self, data):
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def forecastInterval(self, data, **kwargs):
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ndata = np.array(self.doTransformations(data))
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@ -53,10 +53,10 @@ class EnsembleFTS(fts.FTS):
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return ret
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def forecastAhead(self, data, steps):
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def forecastAhead(self, data, steps, **kwargs):
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pass
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def forecastAheadInterval(self, data, steps):
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def forecastAheadInterval(self, data, steps, **kwargs):
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pass
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@ -88,6 +88,6 @@ class EnsembleFTS(fts.FTS):
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grid[k] += 1
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return grid
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def forecastAheadDistribution(self, data, steps, resolution, parameters=2):
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def forecastAheadDistribution(self, data, steps, **kwargs):
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pass
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fts.py
14
fts.py
@ -4,7 +4,7 @@ from pyFTS import tree
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from pyFTS.common import FuzzySet, SortedCollection
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class FTS(object):
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def __init__(self, order, name):
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def __init__(self, order, name, **kwargs):
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self.sets = {}
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self.flrgs = {}
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self.order = order
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@ -38,22 +38,22 @@ class FTS(object):
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return best
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def forecast(self, data):
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def forecast(self, data, **kwargs):
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pass
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def forecastInterval(self, data):
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def forecastInterval(self, data, **kwargs):
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pass
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def forecastDistribution(self, data):
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def forecastDistribution(self, data, **kwargs):
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pass
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def forecastAhead(self, data, steps):
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def forecastAhead(self, data, steps, **kwargs):
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pass
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def forecastAheadInterval(self, data, steps):
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def forecastAheadInterval(self, data, steps, **kwargs):
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pass
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def forecastAheadDistribution(self, data, steps):
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def forecastAheadDistribution(self, data, steps, **kwargs):
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pass
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def train(self, data, sets, order=1, parameters=None):
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4
hofts.py
4
hofts.py
@ -39,7 +39,7 @@ class HighOrderFLRG(object):
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class HighOrderFTS(fts.FTS):
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def __init__(self, name):
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def __init__(self, order, **kwargs):
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super(HighOrderFTS, self).__init__(1, "HOFTS" + name)
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self.name = "High Order FTS"
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self.shortname = "HOFTS" + name
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@ -79,7 +79,7 @@ class HighOrderFTS(fts.FTS):
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ret = np.array([self.setsDict[s].centroid for s in flrg.RHS])
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return ret
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def forecast(self, data):
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def forecast(self, data, **kwargs):
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ret = []
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4
hwang.py
4
hwang.py
@ -4,7 +4,7 @@ from pyFTS import fts
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class HighOrderFTS(fts.FTS):
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def __init__(self, name):
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def __init__(self, order, **kwargs):
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super(HighOrderFTS, self).__init__(1, name)
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self.isHighOrder = True
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self.minOrder = 2
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@ -12,7 +12,7 @@ class HighOrderFTS(fts.FTS):
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self.shortname = "Hwang" + name
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self.detail = "Hwang"
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def forecast(self, data):
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def forecast(self, data, **kwargs):
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ndata = self.doTransformations(data)
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4
ifts.py
4
ifts.py
@ -7,7 +7,7 @@ from pyFTS import hofts, fts, tree
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class IntervalFTS(hofts.HighOrderFTS):
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def __init__(self, name):
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def __init__(self, order, **kwargs):
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super(IntervalFTS, self).__init__("IFTS " + name)
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self.shortname = "IFTS " + name
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self.name = "Interval FTS"
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@ -47,7 +47,7 @@ class IntervalFTS(hofts.HighOrderFTS):
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for child in node.getChildren():
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self.buildTree(child, lags, level + 1)
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def forecastInterval(self, data):
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def forecastInterval(self, data, **kwargs):
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ndata = np.array(self.doTransformations(data))
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@ -33,7 +33,7 @@ class ImprovedWeightedFLRG(object):
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class ImprovedWeightedFTS(fts.FTS):
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def __init__(self, name):
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def __init__(self, order, **kwargs):
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super(ImprovedWeightedFTS, self).__init__(1, "IWFTS " + name)
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self.name = "Improved Weighted FTS"
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self.detail = "Ismail & Efendi"
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@ -64,7 +64,7 @@ class ImprovedWeightedFTS(fts.FTS):
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ret = np.array([self.setsDict[s].centroid for s in flrg.RHS])
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return ret
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def forecast(self, data):
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def forecast(self, data, **kwargs):
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l = 1
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data = np.array(data)
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@ -24,7 +24,7 @@ class ContextualSeasonalFLRG(object):
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class ContextualMultiSeasonalFTS(sfts.SeasonalFTS):
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def __init__(self, name, indexer):
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def __init__(self, order, name, **kwargs):
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super(ContextualMultiSeasonalFTS, self).__init__("CMSFTS")
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self.name = "Contextual Multi Seasonal FTS"
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self.shortname = "CMSFTS " + name
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@ -62,7 +62,7 @@ class ContextualMultiSeasonalFTS(sfts.SeasonalFTS):
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else:
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return np.array([data.centroid])
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def forecast(self, data):
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def forecast(self, data, **kwargs):
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ret = []
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@ -83,7 +83,7 @@ class ContextualMultiSeasonalFTS(sfts.SeasonalFTS):
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return ret
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def forecastAhead(self, data, steps):
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def forecastAhead(self, data, steps, **kwargs):
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ret = []
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for i in steps:
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flrg = self.flrgs[str(i)]
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@ -4,7 +4,7 @@ from pyFTS import fts, sfts
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class MultiSeasonalFTS(sfts.SeasonalFTS):
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def __init__(self, name, indexer):
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def __init__(self, order, name, **kwargs):
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super(MultiSeasonalFTS, self).__init__("MSFTS")
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self.name = "Multi Seasonal FTS"
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self.shortname = "MSFTS " + name
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@ -36,7 +36,7 @@ class MultiSeasonalFTS(sfts.SeasonalFTS):
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flrs = FLR.generateIndexedFLRs(self.sets, self.indexer, data)
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self.flrgs = self.generateFLRG(flrs)
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def forecast(self, data):
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def forecast(self, data, **kwargs):
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ret = []
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@ -55,7 +55,7 @@ class MultiSeasonalFTS(sfts.SeasonalFTS):
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return ret
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def forecastAhead(self, data, steps):
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def forecastAhead(self, data, steps, **kwargs):
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ret = []
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for i in steps:
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flrg = self.flrgs[str(i)]
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12
pwfts.py
12
pwfts.py
@ -42,7 +42,7 @@ class ProbabilisticWeightedFLRG(hofts.HighOrderFLRG):
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class ProbabilisticWeightedFTS(ifts.IntervalFTS):
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def __init__(self, name, update=True):
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def __init__(self, order, name, **kwargs):
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super(ProbabilisticWeightedFTS, self).__init__("PWFTS")
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self.shortname = "PWFTS " + name
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self.name = "Probabilistic FTS"
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@ -191,7 +191,7 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
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ret = sum(np.array([pi * s.lower for s in flrg.LHS]))
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return ret
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def forecast(self, data):
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def forecast(self, data, **kwargs):
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ndata = np.array(self.doTransformations(data))
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@ -296,7 +296,7 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
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return ret
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def forecastInterval(self, data):
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def forecastInterval(self, data, **kwargs):
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ndata = np.array(self.doTransformations(data))
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@ -402,7 +402,7 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
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return ret
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def forecastAhead(self, data, steps):
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def forecastAhead(self, data, steps, **kwargs):
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ret = [data[k] for k in np.arange(len(data) - self.order, len(data))]
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for k in np.arange(self.order - 1, steps):
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@ -416,7 +416,7 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
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return ret
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def forecastAheadInterval(self, data, steps):
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def forecastAheadInterval(self, data, steps, **kwargs):
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l = len(data)
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@ -464,7 +464,7 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
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grid[k] += 1
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return grid
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def forecastAheadDistribution(self, data, steps, resolution, parameters=2):
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def forecastAheadDistribution(self, data, steps, **kwargs):
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ret = []
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@ -36,7 +36,7 @@ class ExponentialyWeightedFLRG(object):
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class ExponentialyWeightedFTS(fts.FTS):
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def __init__(self, name):
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def __init__(self, order, **kwargs):
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super(ExponentialyWeightedFTS, self).__init__(1, "EWFTS")
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self.name = "Exponentialy Weighted FTS"
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self.detail = "Sadaei"
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@ -60,7 +60,7 @@ class ExponentialyWeightedFTS(fts.FTS):
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flrs = FLR.generateRecurrentFLRs(tmpdata)
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self.flrgs = self.generateFLRG(flrs, self.c)
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||||
|
||||
def forecast(self, data):
|
||||
def forecast(self, data, **kwargs):
|
||||
l = 1
|
||||
|
||||
data = np.array(data)
|
||||
|
4
sfts.py
4
sfts.py
@ -26,7 +26,7 @@ class SeasonalFLRG(FLR.FLR):
|
||||
|
||||
|
||||
class SeasonalFTS(fts.FTS):
|
||||
def __init__(self, name):
|
||||
def __init__(self, order, **kwargs):
|
||||
super(SeasonalFTS, self).__init__(1, "SFTS")
|
||||
self.name = "Seasonal FTS"
|
||||
self.detail = "Chen"
|
||||
@ -60,7 +60,7 @@ class SeasonalFTS(fts.FTS):
|
||||
flrs = FLR.generateRecurrentFLRs(tmpdata)
|
||||
self.flrgs = self.generateFLRG(flrs)
|
||||
|
||||
def forecast(self, data):
|
||||
def forecast(self, data, **kwargs):
|
||||
|
||||
ndata = np.array(self.doTransformations(data))
|
||||
|
||||
|
6
yu.py
6
yu.py
@ -4,7 +4,7 @@ from pyFTS import fts
|
||||
|
||||
|
||||
class WeightedFLRG(fts.FTS):
|
||||
def __init__(self, LHS):
|
||||
def __init__(self, order, **kwargs):
|
||||
self.LHS = LHS
|
||||
self.RHS = []
|
||||
self.count = 1.0
|
||||
@ -31,7 +31,7 @@ class WeightedFLRG(fts.FTS):
|
||||
|
||||
|
||||
class WeightedFTS(fts.FTS):
|
||||
def __init__(self, name):
|
||||
def __init__(self, order, **kwargs):
|
||||
super(WeightedFTS, self).__init__(1, "WFTS " + name)
|
||||
self.name = "Weighted FTS"
|
||||
self.detail = "Yu"
|
||||
@ -53,7 +53,7 @@ class WeightedFTS(fts.FTS):
|
||||
flrs = FLR.generateRecurrentFLRs(tmpdata)
|
||||
self.flrgs = self.generateFLRG(flrs)
|
||||
|
||||
def forecast(self, data):
|
||||
def forecast(self, data, **kwargs):
|
||||
l = 1
|
||||
|
||||
data = np.array(data)
|
||||
|
Loading…
Reference in New Issue
Block a user