diff --git a/pifts.py b/pifts.py
new file mode 100644
index 0000000..1a12fcf
--- /dev/null
+++ b/pifts.py
@@ -0,0 +1,133 @@
+import numpy as np
+from pyFTS import *
+
+class ProbabilisticIntervalFLRG:
+	def __init__(self,order):
+		self.LHS = []
+		self.RHS = []
+		self.RHSfreqs = {}
+		self.order = order
+		self.frequencyCount = 0
+		self.strlhs = ""
+
+	def appendRHS(self,c):
+		self.RHS.append(c)
+		self.frequencyCount = self.frequencyCount + 1
+		if c.name in self.RHSfreqs:
+			self.RHSfreqs[c.name] = self.RHSfreqs[c.name] + 1
+		else:
+			self.RHSfreqs[c.name] = 1
+		
+	def strLHS(self):
+		if len(self.strlhs) == 0:
+			for c in self.LHS:
+				if len(self.strlhs) > 0:
+					self.strlhs = self.strlhs + ","
+				self.strlhs = self.strlhs + c.name
+		return self.strlhs
+	
+	def appendLHS(self,c):
+		self.LHS.append(c)
+
+	def __str__(self):
+		tmp = ""
+		for c in sorted(self.RHS, key=lambda s: s.name):
+			if len(tmp) > 0:
+				tmp = tmp + ","
+			tmp = tmp + c.name
+		return self.strLHS() + " -> " + tmp
+
+class ProbabilisticIntervalFTS(ifts.IntervalFTS):
+	def __init__(self,name):
+		super(IntervalFTS, self).__init__(1,name)
+		self.flrgs = {}
+		self.globalFrequency = 0
+		
+	def generateFLRG(self, flrs):
+		flrgs = {}
+		l = len(flrs)
+		for k in np.arange(self.order +1, l):
+			flrg = ProbabilisticIntervalFLRG(self.order)
+			
+			for kk in np.arange(k - self.order, k):
+				flrg.appendLHS( flrs[kk].LHS )
+						
+			if flrg.strLHS() in flrgs:
+				flrgs[flrg.strLHS()].appendRHS(flrs[k].RHS)
+			else:
+				flrgs[flrg.strLHS()] = flrg;
+				flrgs[flrg.strLHS()].appendRHS(flrs[k].RHS)
+				
+			self.globalFrequency = self.globalFrequency + 1
+		return (flrgs)
+    	
+	def forecast(self,data):
+		
+		ndata = np.array(data)
+		
+		l = len(ndata)
+		
+		ret = []
+		
+		for k in np.arange(self.order,l):
+			
+			print(k)
+			
+			flrs = []
+			mvs = []
+			
+			up = []
+			lo = []
+			
+			# Achar os conjuntos que tem pert > 0 para cada lag
+			count = 0
+			lags = {}
+			if self.order > 1:
+				subset = ndata[k-self.order : k ]
+				print(subset)
+				for instance in subset:
+					mb = common.fuzzyInstance(instance, self.sets)
+					tmp = np.argwhere( mb )
+					idx = np.ravel(tmp) #flat the array
+					lags[count] = idx 
+					count = count + 1				
+					
+				# Constrói uma árvore com todos os caminhos possíveis
+				
+				root = tree.FLRGTreeNode(None)
+				
+				self.buildTree(root,lags,0)
+				
+				# Traça os possíveis caminhos e costrói as HOFLRG's
+				
+				for p in root.paths():
+					path = list(reversed(list(filter(None.__ne__, p))))
+					flrg = hofts.HighOrderFLRG(self.order)
+					for kk in path: flrg.appendLHS(self.sets[ kk ])
+					
+					flrs.append(flrg)
+					
+					# Acha a pertinência geral de cada FLRG
+					mvs.append(min(self.getSequenceMembership(subset, flrg.LHS)))
+			else:
+				
+				mv = common.fuzzyInstance(ndata[k],self.sets)
+				tmp = np.argwhere( mv )
+				idx = np.ravel(tmp)
+				for kk in idx:
+					flrg = hofts.HighOrderFLRG(self.order)
+					flrg.appendLHS(self.sets[ kk ])
+					flrs.append(flrg)
+					mvs.append(mv[kk])
+			
+			count = 0
+			for flrg in flrs:
+				# achar o os bounds de cada FLRG, ponderados pela pertinência
+				up.append( mvs[count] * self.getUpper(flrg) )
+				lo.append( mvs[count] * self.getLower(flrg) )
+				count = count + 1
+			
+			# gerar o intervalo
+			ret.append( [ sum(lo), sum(up) ] )
+				
+		return ret