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PIFTS forecast n steps ahead

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Petrônio Cândido de Lima e Silva 2016-11-01 16:03:10 -02:00
parent 3f757fb977
commit e336a0670e
2 changed files with 56 additions and 9 deletions
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49
benchmarks.py
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@ -46,7 +46,7 @@ def coverage(targets,forecasts):
if targets[i] >= forecasts[i][0] and targets[i] <= forecasts[i][1] :
preds.append(1)
else:
preds.append(1)
preds.append(0)
return np.mean(preds)
def getIntervalStatistics(original,models):
@ -54,11 +54,11 @@ def getIntervalStatistics(original,models):
for fts in models:
forecasts = fts.forecast(original)
ret = ret + fts.shortname + " & "
ret = ret + str( round(rmse_interval(original[fts.order :],forecasts),2)) + " & "
ret = ret + str( round(mape_interval(original[fts.order :],forecasts),2)) + " & "
ret = ret + str( round(rmse_interval(original[fts.order-1 :],forecasts),2)) + " & "
ret = ret + str( round(mape_interval(original[fts.order-1 :],forecasts),2)) + " & "
ret = ret + str( round(sharpness(forecasts),2)) + " & "
ret = ret + str( round(resolution(forecasts),2)) + " & "
ret = ret + str( round(coverage(original[fts.order :],forecasts),2)) + " \\ \n"
ret = ret + str( round(coverage(original[fts.order-1 :],forecasts),2)) + " \\ \n"
return ret
def plotComparedSeries(original,models, colors):
@ -97,9 +97,48 @@ def plotComparedSeries(original,models, colors):
ax.set_ylabel('F(T)')
ax.set_xlabel('T')
ax.set_xlim([0,len(original)])
def plotComparedIntervalsAhead(original,models, colors, time_from, time_to):
fig = plt.figure(figsize=[25,10])
ax = fig.add_subplot(111)
mi = []
ma = []
def plotCompared(original,forecasted,labels,title):
ax.plot(original,color='black',label="Original")
count = 0
for fts in models:
if fts.isInterval:
forecasted = fts.forecastAhead(original[:time_from],time_to)
lower = [kk[0] for kk in forecasted]
upper = [kk[1] for kk in forecasted]
mi.append(min(lower))
ma.append(max(upper))
for k in np.arange(0,time_from):
lower.insert(0,None)
upper.insert(0,None)
ax.plot(lower,color=colors[count],label=fts.shortname)
ax.plot(upper,color=colors[count])
else:
forecasted = fts.forecast(original)
mi.append(min(forecasted))
ma.append(max(forecasted))
forecasted.insert(0,None)
ax.plot(forecasted,color=colors[count],label=fts.shortname)
handles0, labels0 = ax.get_legend_handles_labels()
ax.legend(handles0,labels0)
count = count + 1
#ax.set_title(fts.name)
ax.set_ylim([min(mi),max(ma)])
ax.set_ylabel('F(T)')
ax.set_xlabel('T')
ax.set_xlim([0,len(original)])
def plotCompared(original,forecasts,labels,title):
fig = plt.figure(figsize=[13,6])
ax = fig.add_subplot(111)
ax.plot(original,color='k',label="Original")

16
pifts.py
View File

@ -83,7 +83,7 @@ class ProbabilisticIntervalFTS(ifts.IntervalFTS):
ret = []
for k in np.arange(self.order,l):
for k in np.arange(self.order-1,l):
affected_flrgs = []
affected_flrgs_memberships = []
@ -96,8 +96,7 @@ class ProbabilisticIntervalFTS(ifts.IntervalFTS):
count = 0
lags = {}
if self.order > 1:
subset = ndata[k-self.order : k ]
subset = ndata[k-(self.order-1) : k+1 ]
for instance in subset:
mb = common.fuzzyInstance(instance, self.sets)
tmp = np.argwhere( mb )
@ -148,7 +147,16 @@ class ProbabilisticIntervalFTS(ifts.IntervalFTS):
ret.append( [ sum(lo)/norm, sum(up)/norm ] )
return ret
def forecastAhead(self,data,steps):
ret = [[data[k],data[k]] for k in np.arange(len(data)-self.order,len(data))]
for k in np.arange(self.order,steps):
lower = self.forecast( [ret[x][0] for x in np.arange(k-self.order,k)] )
upper = self.forecast( [ret[x][1] for x in np.arange(k-self.order,k)] )
ret.append([np.min(lower),np.max(upper)])
return ret
def __str__(self):
tmp = self.name + ":\n"
for r in sorted(self.flrgs):