Bugfix at fts.forecastAhead; forecastDistribution with ProbabilityDistribution on benchmarks.arima
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Petrônio Cândido
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5503cd26a4
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8fea727560
@ -8,6 +8,7 @@ import time
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import numpy as np
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import pandas as pd
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from pyFTS.common import FuzzySet,SortedCollection
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from pyFTS.probabilistic import ProbabilityDistribution
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def acf(data, k):
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@ -154,7 +155,7 @@ def resolution(forecasts):
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def coverage(targets, forecasts):
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"""Percent of"""
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"""Percent of target values that fall inside forecasted interval"""
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preds = []
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for i in np.arange(0, len(forecasts)):
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if targets[i] >= forecasts[i][0] and targets[i] <= forecasts[i][1]:
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@ -218,14 +219,25 @@ def heavyside_cdf(bins, targets):
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def crps(targets, densities):
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"""Continuous Ranked Probability Score"""
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l = len(densities.columns)
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n = len(densities.index)
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Ff = pmf_to_cdf(densities)
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Fa = heavyside_cdf(densities.columns, targets)
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_crps = float(0.0)
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for k in densities.index:
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_crps += sum([ (Ff[col][k]-Fa[col][k])**2 for col in densities.columns])
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if isinstance(densities, pd.DataFrame):
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l = len(densities.columns)
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n = len(densities.index)
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Ff = pmf_to_cdf(densities)
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Fa = heavyside_cdf(densities.columns, targets)
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for k in densities.index:
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_crps += sum([ (Ff[col][k]-Fa[col][k])**2 for col in densities.columns])
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elif isinstance(densities, ProbabilityDistribution):
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l = len(densities.bins)
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n = 1
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Fa = heavyside_cdf(densities.bin, targets)
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_crps = sum([(densities.cdf(val) - Fa[val][0]) ** 2 for val in densities.bins])
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elif isinstance(densities, list):
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l = len(densities[0].bins)
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n = len(densities)
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Fa = heavyside_cdf(densities[0].bin, targets)
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for df in densities:
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_crps += sum([(df.cdf(val) - Fa[val][0]) ** 2 for val in df.bins])
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return _crps / float(l * n)
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@ -7,6 +7,7 @@ from statsmodels.tsa.arima_model import ARIMA as stats_arima
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import scipy.stats as st
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from pyFTS import fts
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from pyFTS.common import SortedCollection
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from pyFTS.probabilistic import ProbabilityDistribution
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class ARIMA(fts.FTS):
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@ -148,29 +149,55 @@ class ARIMA(fts.FTS):
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def empty_grid(self, resolution):
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return self.get_empty_grid(-(self.original_max*2), self.original_max*2, resolution)
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def forecastDistribution(self, data, **kwargs):
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sigma = np.sqrt(self.model_fit.sigma2)
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l = len(data)
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ret = []
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for k in np.arange(self.order, l + 1):
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tmp = []
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sample = [data[i] for i in np.arange(k - self.order, k)]
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mean = self.forecast(sample)
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if isinstance(mean, (list, np.ndarray)):
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mean = mean[0]
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dist = ProbabilityDistribution.ProbabilityDistribution(type="histogram", uod=[self.original_min, self.original_max])
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intervals = []
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for alpha in np.arange(0.05, 0.5, 0.05):
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qt1 = mean + st.norm.ppf(alpha) * sigma
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qt2 = mean + st.norm.ppf(1 - alpha) * sigma
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intervals.append([qt1, qt2])
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dist.appendInterval(intervals)
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ret.append(dist)
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return ret
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def forecastAheadDistribution(self, data, steps, **kwargs):
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smoothing = kwargs.get("smoothing", 0.5)
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sigma = np.sqrt(self.model_fit.sigma2)
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ndata = np.array(self.doTransformations(data))
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l = len(ndata)
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percentile_size = (self.original_max - self.original_min)/100
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resolution = kwargs.get('resolution', percentile_size)
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grid = self.empty_grid(resolution)
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index = SortedCollection.SortedCollection(iterable=grid.keys())
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l = len(data)
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ret = []
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nmeans = self.forecastAhead(ndata, steps, **kwargs)
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nmeans = self.forecastAhead(data, steps, **kwargs)
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for k in np.arange(0, steps):
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grid = self.empty_grid(resolution)
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dist = ProbabilityDistribution.ProbabilityDistribution(type="histogram",
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uod=[self.original_min, self.original_max])
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intervals = []
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for alpha in np.arange(0.05, 0.5, 0.05):
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tmp = []
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@ -179,12 +206,10 @@ class ARIMA(fts.FTS):
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tmp.append(nmeans[k] + st.norm.ppf(alpha) * hsigma)
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tmp.append(nmeans[k] + st.norm.ppf(1 - alpha) * hsigma)
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grid = self.gridCount(grid, resolution, index, tmp)
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intervals.append(tmp)
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tmp = np.array([grid[i] for i in sorted(grid)])
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dist.appendInterval(intervals)
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ret.append(tmp / sum(tmp))
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ret.append(dist)
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grid = self.empty_grid(resolution)
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df = pd.DataFrame(ret, columns=sorted(grid))
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return df
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return ret
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@ -89,19 +89,15 @@ class FTS(object):
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:param kwargs:
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:return:
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"""
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ndata = [k for k in self.doTransformations(data[- self.order:])]
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ret = []
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for k in np.arange(0,steps):
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tmp = self.forecast(ndata[-self.order:], **kwargs)
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tmp = self.forecast(data[-self.order:], **kwargs)
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if isinstance(tmp,(list, np.ndarray)):
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tmp = tmp[0]
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ret.append(tmp)
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ndata.append(tmp)
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ret = self.doInverseTransformations(ret, params=[ndata[self.order - 1:]])
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data.append(tmp)
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return ret
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@ -3,7 +3,8 @@ import pandas as pd
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import matplotlib.pyplot as plt
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from pyFTS.common import FuzzySet,SortedCollection
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from pyFTS.probabilistic import kde
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from pyFTS import tree
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from pyFTS.common import SortedCollection
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class ProbabilityDistribution(object):
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"""
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@ -42,6 +43,10 @@ class ProbabilityDistribution(object):
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self.name = kwargs.get("name", "")
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def set(self, value, density):
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k = self.index.find_ge(value)
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self.distribution[k] = density
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def append(self, values):
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if self.type == "histogram":
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for k in values:
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@ -55,20 +60,46 @@ class ProbabilityDistribution(object):
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for v,d in enumerate(dens):
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self.distribution[self.bins[v]] = d
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def appendInterval(self, intervals):
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if self.type == "histogram":
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for interval in intervals:
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for k in self.index.inside(interval[0], interval[1]):
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self.distribution[k] += 1
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self.count += 1
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def density(self, values):
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ret = []
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for k in values:
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if self.type == "histogram":
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v = self.index.find_ge(k)
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ret.append(self.distribution[v] / self.count)
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else:
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elif self.type == "KDE":
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v = self.kde.probability(k, self.data)
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ret.append(v)
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else:
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v = self.index.find_ge(k)
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ret.append(self.distribution[v])
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return ret
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def cdf(self, value):
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ret = 0
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for k in self.bins:
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if k < value:
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ret += self.distribution[k]
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else:
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return ret
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return ret
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def cummulative(self, values):
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pass
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if isinstance(values, list):
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ret = []
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for k in values:
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ret.append(self.cdf(k))
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else:
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return self.cdf(values)
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def quantile(self, qt):
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pass
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