Bugfixes in FTS.forecast_ahead

pyFTS/common/fts.py

@@ -189,15 +189,19 @@ class FTS(object):
         :param kwargs: model specific parameters
         :return: a list with the forecasted values
         """
+
+        if isinstance(data, np.ndarray):
+            data = data.tolist()
+
         ret = []
         for k in np.arange(0,steps):
-            tmp = self.forecast(data[-self.order:], **kwargs)
+            tmp = self.forecast(data[-self.max_lag:], **kwargs)
 
             if isinstance(tmp,(list, np.ndarray)):
-                tmp = tmp[0]
+                tmp = tmp[-1]
 
             ret.append(tmp)
-            data.append_rhs(tmp)
+            data.append(tmp)
 
         return ret
 

pyFTS/data/Bitcoin.py

@@ -12,7 +12,7 @@ import pandas as pd
 import numpy as np
 
 
-def get_data(field='avg'):
+def get_data(field='AVG'):
     """
     Get the univariate time series data.
 

pyFTS/data/DowJones.py

@@ -12,7 +12,7 @@ import pandas as pd
 import numpy as np
 
 
-def get_data(field='avg'):
+def get_data(field='AVG'):
     """
     Get the univariate time series data.
 

pyFTS/data/Ethereum.py

@@ -12,7 +12,7 @@ import pandas as pd
 import numpy as np
 
 
-def get_data(field='avg'):
+def get_data(field='AVG'):
     """
     Get the univariate time series data.
 

pyFTS/models/hofts.py

@@ -117,7 +117,7 @@ class HighOrderFTS(fts.FTS):
 
         l = len(ndata)
 
-        if l <= self.max_lag:
+        if l < self.max_lag:
             return ndata
 
         for k in np.arange(self.max_lag, l+1):

pyFTS/tests/general.py

@@ -19,7 +19,7 @@ dataset = TAIEX.get_data()
 #print(len(dataset))
 
 from pyFTS.partitioners import Grid, Util as pUtil
-partitioner = Grid.GridPartitioner(data=dataset[:800], npart=10, transformation=tdiff)
+partitioner = Grid.GridPartitioner(data=dataset[:2000], npart=20) #, transformation=tdiff)
 
 
 from pyFTS.common import Util as cUtil
@@ -28,15 +28,23 @@ from pyFTS.benchmarks import benchmarks as bchmk, Util as bUtil, Measures, knn,
 from pyFTS.models import pwfts, song, chen, ifts, hofts
 from pyFTS.models.ensemble import ensemble
 
-model = chen.ConventionalFTS(partitioner=partitioner)
-#model = hofts.HighOrderFTS(partitioner=partitioner,order=2)
-model.append_transformation(tdiff)
-model.fit(dataset[:800])
+print(partitioner)
 
-cUtil.plot_rules(model, size=[20,20], rules_by_axis=5, columns=1)
+#model = chen.ConventionalFTS(partitioner=partitioner)
+model = hofts.HighOrderFTS(partitioner=partitioner,order=2)
+#model.append_transformation(tdiff)
+model.fit(dataset[:2000])
 
 print(model)
 
+print(model.predict([3500, 7000], steps_ahead=5))
+
+
+
+#cUtil.plot_rules(model, size=[20,20], rules_by_axis=5, columns=1)
+
+#
+
 print("fim")