82 lines
2.6 KiB
Python
82 lines
2.6 KiB
Python
#!/usr/bin/python
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# -*- coding: utf8 -*-
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import numpy as np
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from statsmodels.regression.quantile_regression import QuantReg
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from statsmodels.tsa.tsatools import lagmat
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from pyFTS import fts
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class QuantileRegression(fts.FTS):
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"""Façade for statsmodels.regression.quantile_regression"""
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def __init__(self, name, **kwargs):
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super(QuantileRegression, self).__init__(1, "QR"+name)
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self.name = "QR"
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self.detail = "Quantile Regression"
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self.is_high_order = True
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self.has_point_forecasting = True
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self.has_interval_forecasting = True
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self.has_probability_forecasting = True
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self.benchmark_only = True
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self.minOrder = 1
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self.alpha = kwargs.get("alpha", 0.05)
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self.upper_qt = None
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self.mean_qt = None
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self.lower_qt = None
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def train(self, data, sets, order=1, parameters=None):
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self.order = order
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tmp = np.array(self.doTransformations(data))
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lagdata, ndata = lagmat(tmp, maxlag=order, trim="both", original='sep')
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mqt = QuantReg(ndata, lagdata).fit(0.5)
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if self.alpha is not None:
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uqt = QuantReg(ndata, lagdata).fit(1 - self.alpha)
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lqt = QuantReg(ndata, lagdata).fit(self.alpha)
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self.mean_qt = [k for k in mqt.params]
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if self.alpha is not None:
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self.upper_qt = [k for k in uqt.params]
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self.lower_qt = [k for k in lqt.params]
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self.shortname = "QAR(" + str(self.order) + ")"
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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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return sum([data[k] * params[k] for k in np.arange(0, self.order)])
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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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ret = []
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for k in np.arange(self.order, l+1): #+1 to forecast one step ahead given all available lags
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sample = ndata[k - self.order : k]
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ret.append(self.linearmodel(sample, self.mean_qt))
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ret = self.doInverseTransformations(ret, params=[data[self.order - 1:]])
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return ret
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def forecastInterval(self, data, **kwargs):
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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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for k in np.arange(self.order , l):
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sample = ndata[k - self.order: k]
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up = self.linearmodel(sample, self.upper_qt)
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down = self.linearmodel(sample, self.down_qt)
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ret.append([up, down])
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ret = self.doInverseTransformations(ret, params=[data[self.order - 1:]], interval=True)
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return ret
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