- Song & Chissom Conventional FTS

benchmarks/benchmarks.py

@@ -17,7 +17,7 @@ from mpl_toolkits.mplot3d import Axes3D
 from pyFTS.partitioners import partitioner, Grid, Huarng, Entropy, FCM
 from pyFTS.benchmarks import Measures, naive, arima, ResidualAnalysis, ProbabilityDistribution, Util, quantreg
 from pyFTS.common import Membership, FuzzySet, FLR, Transformations, Util
-from pyFTS import fts, chen, yu, ismailefendi, sadaei, hofts, hwang,  pwfts, ifts
+from pyFTS import fts, chen, yu, ismailefendi, sadaei, hofts, hwang,  pwfts, ifts, cheng
 from copy import deepcopy
 
 colors = ['grey', 'rosybrown', 'maroon', 'red','orange', 'yellow', 'olive', 'green',
@@ -31,11 +31,11 @@ nsty = len(styles)
 
 def get_benchmark_point_methods():
     """Return all non FTS methods for point forecast"""
-    return [naive.Naive, arima.ARIMA]
+    return [naive.Naive, arima.ARIMA, quantreg.QuantileRegression]
 
 def get_point_methods():
     """Return all FTS methods for point forecast"""
-    return [chen.ConventionalFTS, yu.WeightedFTS, ismailefendi.ImprovedWeightedFTS,
+    return [chen.ConventionalFTS, yu.WeightedFTS, ismailefendi.ImprovedWeightedFTS, cheng.TrendWeightedFTS,
                   sadaei.ExponentialyWeightedFTS, hofts.HighOrderFTS, pwfts.ProbabilisticWeightedFTS]
 
 def get_benchmark_interval_methods():

benchmarks/quantreg.py

@@ -16,6 +16,7 @@ class QuantileRegression(fts.FTS):
         self.is_high_order = True
         self.has_point_forecasting = True
         self.has_interval_forecasting = True
+        self.has_probability_forecasting = True
         self.benchmark_only = True
         self.minOrder = 1
         self.alpha = 0.5

song.py

@@ -0,0 +1,67 @@
+import numpy as np
+from pyFTS.common import FuzzySet, FLR
+from pyFTS import fts
+
+class ConventionalFTS(fts.FTS):
+    """Conventional Fuzzy Time Series"""
+    def __init__(self, name, **kwargs):
+        super(ConventionalFTS, self).__init__(1, "CFTS " + name)
+        self.name = "Conventional FTS"
+        self.detail = "Song & Chissom"
+        self.R = None
+
+    def flr_membership_matrix(self, flr):
+        lm = [flr.LHS.membership(k.centroid) for k in self.sets]
+        rm = [flr.RHS.membership(k.centroid) for k in self.sets]
+
+        r = np.zeros((len(self.sets), len(self.sets)))
+        for k in range(0,len(self.sets)):
+            for l in range(0, len(self.sets)):
+                r[k][l] = min(lm[k],rm[l])
+
+        return r
+
+    def operation_matrix(self, flrs):
+        r = np.zeros((len(self.sets),len(self.sets)))
+        for k in flrs:
+            mm = self.flr_membership_matrix(k)
+            for k in range(0, len(self.sets)):
+                for l in range(0, len(self.sets)):
+                    r[k][l] = max(r[k][l], mm[k][l])
+
+        return r
+
+    def train(self, data, sets,order=1,parameters=None):
+        self.sets = sets
+        ndata = self.doTransformations(data)
+        tmpdata = FuzzySet.fuzzySeries(ndata, sets)
+        flrs = FLR.generateNonRecurrentFLRs(tmpdata)
+        self.R = self.operation_matrix(flrs)
+
+    def forecast(self, data, **kwargs):
+
+        ndata = np.array(self.doTransformations(data))
+
+        l = len(ndata)
+        npart = len(self.sets)
+
+        ret = []
+
+        for k in np.arange(0, l):
+            mv = FuzzySet.fuzzyInstance(ndata[k], self.sets)
+
+            r = [max([ min(self.R[i][j], mv[j]) for j in np.arange(0,npart) ]) for i in np.arange(0,npart)]
+
+            fs = np.ravel(np.argwhere(r == max(r)))
+
+            if len(fs) == 1:
+                ret.append(self.sets[fs[0]].centroid)
+            else:
+                mp = [self.sets[s].centroid for s in fs]
+
+                ret.append( sum(mp)/len(mp))
+
+
+        ret = self.doInverseTransformations(ret, params=[data[self.order - 1:]])
+
+        return ret

tests/general.py

@@ -17,16 +17,30 @@ from pyFTS.benchmarks import naive, arima
 from pyFTS.benchmarks import Measures
 from numpy import random
 
+os.chdir("/home/petronio/dados/Dropbox/Doutorado/Codigos/")
+
+enrollments = pd.read_csv("DataSets/Enrollments.csv", sep=";")
+enrollments = np.array(enrollments["Enrollments"])
+
+from pyFTS import song
+
+enrollments_fs = Grid.GridPartitioner(enrollments, 10).sets
+
+model = song.ConventionalFTS('')
+model.train(enrollments,enrollments_fs)
+teste = model.forecast(enrollments)
+
+print(teste)
+
+
 #print(FCM.FCMPartitionerTrimf.__module__)
 
 #gauss = random.normal(0,1.0,1000)
 #gauss_teste = random.normal(0,1.0,400)
 
 
-os.chdir("/home/petronio/dados/Dropbox/Doutorado/Codigos/")
-
-taiexpd = pd.read_csv("DataSets/TAIEX.csv", sep=",")
-taiex = np.array(taiexpd["avg"][:5000])
+#taiexpd = pd.read_csv("DataSets/TAIEX.csv", sep=",")
+#taiex = np.array(taiexpd["avg"][:5000])
 
 #from statsmodels.tsa.arima_model import ARIMA as stats_arima
 from statsmodels.tsa.tsatools import lagmat
@@ -38,7 +52,7 @@ from statsmodels.tsa.tsatools import lagmat
 #print(lag)
 #print(a)
 
-from pyFTS.benchmarks import distributed_benchmarks as bchmk
+#from pyFTS.benchmarks import distributed_benchmarks as bchmk
 #from pyFTS.benchmarks import parallel_benchmarks as bchmk
 #from pyFTS.benchmarks import benchmarks as bchmk
 #from pyFTS.benchmarks import arima
@@ -52,11 +66,11 @@ from pyFTS.benchmarks import distributed_benchmarks as bchmk
 
 #bchmk.teste(taiex,['192.168.0.109', '192.168.0.101'])
 
-bchmk.point_sliding_window(taiex,2000,train=0.8, #models=[yu.WeightedFTS], # #
-                     partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ],
-                     partitions= np.arange(10,200,step=5), #transformation=diff,
-                     dump=True, save=False, file="experiments/nasdaq_point_distributed.csv",
-                     nodes=['192.168.1.42']) #, depends=[hofts, ifts])
+#bchmk.point_sliding_window(taiex,2000,train=0.8, #models=[yu.WeightedFTS], # #
+#                     partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ],
+#                     partitions= np.arange(10,200,step=5), #transformation=diff,
+#                     dump=True, save=False, file="experiments/nasdaq_point_distributed.csv",
+#                     nodes=['192.168.1.42']) #, depends=[hofts, ifts])
 
 #bchmk.testa(taiex,[10,20],partitioners=[Grid.GridPartitioner], nodes=['192.168.0.109', '192.168.0.101'])
 
@@ -83,10 +97,10 @@ bchmk.point_sliding_window(taiex,2000,train=0.8, #models=[yu.WeightedFTS], # #
 #                                    gauss,2000,train=0.8, dump=True, save=True, file="experiments/arima_gauss.csv")
 
 
-bchmk.interval_sliding_window(gauss,2000,train=0.8, #transformation=diff, #models=[pwfts.ProbabilisticWeightedFTS], # #
-                     partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ],
-                     partitions= np.arange(10,200,step=5), #
-                     dump=True, save=False, file="experiments/nasdaq_interval.csv")
+#bchmk.interval_sliding_window(gauss,2000,train=0.8, #transformation=diff, #models=[pwfts.ProbabilisticWeightedFTS], # #
+#                     partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ],
+#                     partitions= np.arange(10,200,step=5), #
+#                     dump=True, save=False, file="experiments/nasdaq_interval.csv")
 
 #3bchmk.ahead_sliding_window(taiex,2000,train=0.8, steps=20, resolution=250, #transformation=diff, #models=[pwfts.ProbabilisticWeightedFTS], # #
 #                    partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ],