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PIFTS - Density forecast

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Petrônio Cândido de Lima e Silva 2016-11-05 21:24:36 -02:00
parent ccdc74a49d
commit d27141df81
2 changed files with 50 additions and 1 deletions
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7
benchmarks.py
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@ -61,6 +61,13 @@ def getIntervalStatistics(original,models):
ret = ret + str( round(coverage(original[fts.order-1 :],forecasts),2)) + " \\ \n" ret = ret + str( round(coverage(original[fts.order-1 :],forecasts),2)) + " \\ \n"
return ret return ret
def plotDistribution(dist):
for k in dist.index:
alpha = np.array([dist[x][k] for x in dist])*100
x = [k for x in np.arange(0,len(alpha))]
y = dist.columns
plt.scatter(x,y,c=alpha,marker='s',linewidths=0,cmap='Reds',edgecolors=None)
def plotComparedSeries(original,models, colors): def plotComparedSeries(original,models, colors):
fig = plt.figure(figsize=[25,10]) fig = plt.figure(figsize=[25,10])
ax = fig.add_subplot(111) ax = fig.add_subplot(111)

42
pifts.py
View File

@ -1,4 +1,5 @@
import numpy as np import numpy as np
import pandas as pd
from pyFTS import * from pyFTS import *
class ProbabilisticFLRG(hofts.HighOrderFLRG): class ProbabilisticFLRG(hofts.HighOrderFLRG):
@ -79,6 +80,8 @@ class ProbabilisticIntervalFTS(ifts.IntervalFTS):
ndata = np.array(data) ndata = np.array(data)
#print(ndata)
l = len(ndata) l = len(ndata)
ret = [] ret = []
@ -172,6 +175,45 @@ class ProbabilisticIntervalFTS(ifts.IntervalFTS):
return ret return ret
def getGridClean(self,resolution):
grid = {}
for sbin in np.arange(self.sets[0].lower,self.sets[-1].upper,resolution):
grid[sbin] = 0
return grid
def gridCount(self, grid, resolution, interval):
for sbin in sorted(grid):
if sbin >= interval[0] and (sbin + resolution) <= interval[1]:
grid[sbin] = grid[sbin] + 1
return grid
def forecastDistributionAhead(self,data,steps,resolution):
ret = []
intervals = self.forecastAhead(data,steps)
for k in np.arange(self.order,steps):
grid = self.getGridClean(resolution)
qt1st = self.forecast([intervals[x][0] + (intervals[x][1]-intervals[x][0])/4 for x in np.arange(k-self.order,k)] )
qt2nd = self.forecast([intervals[x][0] + (intervals[x][1]-intervals[x][0])/2 for x in np.arange(k-self.order,k)] )
qt3rd = self.forecast([intervals[x][1] - (intervals[x][1]-intervals[x][0])/4 for x in np.arange(k-self.order,k)] )
grid = self.gridCount(grid,resolution, intervals[k])
grid = self.gridCount(grid,resolution, np.ravel(qt1st))
grid = self.gridCount(grid,resolution, np.ravel(qt2nd))
grid = self.gridCount(grid,resolution, np.ravel(qt3rd))
tmp = np.array([ grid[k] for k in sorted(grid) ])
ret.append( tmp/sum(tmp) )
grid = self.getGridClean(resolution)
df = pd.DataFrame(ret, columns=sorted(grid))
return df
def __str__(self): def __str__(self):
tmp = self.name + ":\n" tmp = self.name + ":\n"
for r in sorted(self.flrgs): for r in sorted(self.flrgs):