CVFTS and NSFTS unification
This commit is contained in:
Petrônio Cândido
parent
f3b399eb97
commit
9be5c611a7
@ -413,7 +413,7 @@ class FTS(object):
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def __str__(self):
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tmp = self.name + ":\n"
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for r in sorted(self.flrgs):
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for r in sorted(self.flrgs, key=lambda key: self.flrgs[key].get_midpoint(self.sets)):
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tmp = tmp + str(self.flrgs[r]) + "\n"
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return tmp
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@ -3,6 +3,7 @@ from pyFTS.models import hofts
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from pyFTS.models.nonstationary import common,nsfts
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from pyFTS.common import FLR, flrg, tree
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class HighOrderNonstationaryFLRG(hofts.HighOrderFTS):
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"""Conventional High Order Fuzzy Logical Relationship Group"""
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def __init__(self, order, **kwargs):
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@ -57,7 +58,9 @@ class ConditionalVarianceFTS(hofts.HighOrderFTS):
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def train(self, ndata, **kwargs):
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tmpdata = common.fuzzySeries(ndata, self.sets, self.partitioner.ordered_sets, method='fuzzy', const_t=0)
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tmpdata = common.fuzzySeries(ndata, self.sets,
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self.partitioner.ordered_sets,
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method='fuzzy', const_t=0)
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flrs = FLR.generate_non_recurrent_flrs(tmpdata)
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self.generate_flrg(flrs)
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@ -75,17 +78,17 @@ class ConditionalVarianceFTS(hofts.HighOrderFTS):
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def generate_flrg(self, flrs, **kwargs):
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for flr in flrs:
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if flr.LHS.name in self.flrgs:
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self.flrgs[flr.LHS.name].append_rhs(flr.RHS)
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self.flrgs[flr.LHS.name].append_rhs(flr.RHS.name)
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else:
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self.flrgs[flr.LHS.name] = nsfts.ConventionalNonStationaryFLRG(flr.LHS)
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self.flrgs[flr.LHS.name].append_rhs(flr.RHS)
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self.flrgs[flr.LHS.name] = nsfts.ConventionalNonStationaryFLRG(flr.LHS.name)
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self.flrgs[flr.LHS.name].append_rhs(flr.RHS.name)
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def _smooth(self, a):
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return .1 * a[0] + .3 * a[1] + .6 * a[2]
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def perturbation_factors(self, data, **kwargs):
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npart = len(self.partitioner.sets)
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_max = 0
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_min = 0
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if data < self.original_min:
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@ -101,7 +104,7 @@ class ConditionalVarianceFTS(hofts.HighOrderFTS):
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_range = (_max - _min)/2
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translate = np.linspace(_min, _max, len(self.partitioner.sets))
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translate = np.linspace(_min, _max, npart)
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var = np.std(self.residuals)
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@ -109,13 +112,18 @@ class ConditionalVarianceFTS(hofts.HighOrderFTS):
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loc = (self.mean_residual + np.mean(self.residuals))
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location = [_range + w + loc + k for k in np.linspace(-var,var) for w in translate]
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location = [_range + w + loc + k for k in np.linspace(-var,var, npart) for w in translate]
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perturb = [[location[k], var] for k in np.arange(len(self.partitioner.sets))]
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scale = [abs(location[0] - location[2])]
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scale.extend([abs(location[k - 1] - location[k + 1]) for k in np.arange(1, npart)])
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scale.append(abs(location[-1] - location[-3]))
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perturb = [[location[k], scale[k]] for k in np.arange(npart)]
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return perturb
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def perturbation_factors__old(self, data):
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npart = len(self.partitioner.sets)
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_max = 0
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_min = 0
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if data < self.original_min:
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@ -129,12 +137,12 @@ class ConditionalVarianceFTS(hofts.HighOrderFTS):
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self.max_stack.insert(0, _max)
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_max = max(self.max_stack)
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location = np.linspace(_min, _max, self.partitioner.partitions)
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location = np.linspace(_min, _max, npart)
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scale = [abs(location[0] - location[2])]
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scale.extend([abs(location[k-1] - location[k+1]) for k in np.arange(1,self.partitioner.partitions-1)])
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scale.extend([abs(location[k-1] - location[k+1]) for k in np.arange(1, npart)])
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scale.append(abs(location[-1] - location[-3]))
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perturb = [[location[k], scale[k]] for k in np.arange(0, self.partitioner.partitions)]
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perturb = [[location[k], scale[k]] for k in np.arange(0, npart)]
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return perturb
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@ -144,16 +152,16 @@ class ConditionalVarianceFTS(hofts.HighOrderFTS):
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def _affected_sets(self, sample, perturb):
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affected_sets = [[ct, self.sets[self._fsset_key(ct)].membership(sample, perturb[ct])]
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for ct in np.arange(self.partitioner.partitions)
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for ct in np.arange(len(self.partitioner.sets))
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if self.sets[self._fsset_key(ct)].membership(sample, perturb[ct]) > 0.0]
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if len(affected_sets) == 0:
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if sample < self.partitioner.lower_set().get_lower(perturb[0]):
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affected_sets.append([0, 1])
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elif sample > self.partitioner.upper_set().get_upper(perturb[-1]):
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affected_sets.append([len(self.sets) - 1, 1])
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return affected_sets
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def forecast(self, ndata, **kwargs):
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@ -181,7 +189,7 @@ class ConditionalVarianceFTS(hofts.HighOrderFTS):
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ix = affected_sets[0][0]
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aset = self.partitioner.ordered_sets[ix]
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if aset in self.flrgs:
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numerator.append(self.flrgs[aset].get_midpoint(perturb[ix]))
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numerator.append(self.flrgs[aset].get_midpoint(self.sets, perturb[ix]))
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else:
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fuzzy_set = self.sets[aset]
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numerator.append(fuzzy_set.get_midpoint(perturb[ix]))
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@ -192,7 +200,7 @@ class ConditionalVarianceFTS(hofts.HighOrderFTS):
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fs = self.partitioner.ordered_sets[ix]
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tdisp = perturb[ix]
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if fs in self.flrgs:
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numerator.append(self.flrgs[fs].get_midpoint(tdisp) * aset[1])
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numerator.append(self.flrgs[fs].get_midpoint(self.sets, tdisp) * aset[1])
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else:
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fuzzy_set = self.sets[fs]
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numerator.append(fuzzy_set.get_midpoint(tdisp) * aset[1])
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@ -1,9 +1,23 @@
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from pyFTS.common import flrg
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from pyFTS.models.nonstationary import common
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import numpy as np
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class NonStationaryFLRG(flrg.FLRG):
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def unpack_args(self, *args):
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l = len(args)
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tmp = args
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sets, t, w = None, 0, 1
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if l > 0 and isinstance(tmp[0], dict):
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sets = tmp[0]
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if l > 1 and isinstance(tmp[1], (int, list, tuple)):
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t = tmp[1]
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if l > 2 and isinstance(tmp[2], int):
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w = tmp[2]
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return (sets, t, w)
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def __init__(self, LHS, **kwargs):
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super(NonStationaryFLRG, self).__init__(1, **kwargs)
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@ -12,45 +26,49 @@ class NonStationaryFLRG(flrg.FLRG):
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def get_key(self):
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if isinstance(self.LHS, list):
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return str([k.name for k in self.LHS])
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return str([k for k in self.LHS])
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elif isinstance(self.LHS, dict):
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return str(self.LHS.keys())
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else:
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return self.LHS.name
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return self.LHS
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def get_membership(self, data, t, window_size=1):
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def get_membership(self, data, *args):
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sets, t, window_size = self.unpack_args(*args)
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ret = 0.0
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if isinstance(self.LHS, (list, set)):
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#assert len(self.LHS) == len(data)
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ret = min([self.LHS[ct].membership(dat, common.window_index(t - (self.order - ct), window_size))
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ret = min([sets[self.LHS[ct]].membership(dat, common.window_index(t - (self.order - ct), window_size))
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for ct, dat in enumerate(data)])
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else:
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ret = self.LHS.membership(data, common.window_index(t, window_size))
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return ret
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def get_midpoint(self, t, window_size=1):
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def get_midpoint(self, *args):
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sets, t, window_size = self.unpack_args(*args)
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if len(self.RHS) > 0:
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if isinstance(self.RHS, (list, set)):
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tmp = [r.get_midpoint(common.window_index(t, window_size)) for r in self.RHS]
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tmp = [sets[r].get_midpoint(common.window_index(t, window_size)) for r in self.RHS]
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elif isinstance(self.RHS, dict):
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tmp = [self.RHS[r].get_midpoint(common.window_index(t, window_size)) for r in self.RHS.keys()]
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tmp = [sets[r].get_midpoint(common.window_index(t, window_size)) for r in self.RHS.keys()]
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return sum(tmp) / len(tmp)
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else:
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return self.LHS[-1].get_midpoint(common.window_index(t, window_size))
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return sets[self.LHS[-1]].get_midpoint(common.window_index(t, window_size))
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def get_lower(self, t, window_size=1):
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def get_lower(self, *args):
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sets, t, window_size = self.unpack_args(*args)
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if len(self.RHS) > 0:
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if isinstance(self.RHS, (list, set)):
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return min([r.get_lower(common.window_index(t, window_size)) for r in self.RHS])
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return min([sets[r].get_lower(common.window_index(t, window_size)) for r in self.RHS])
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elif isinstance(self.RHS, dict):
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return min([self.RHS[r].get_lower(common.window_index(t, window_size)) for r in self.RHS.keys()])
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return min([sets[r].get_lower(common.window_index(t, window_size)) for r in self.RHS.keys()])
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else:
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return self.LHS[-1].get_lower(common.window_index(t, window_size))
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return sets[self.LHS[-1]].get_lower(common.window_index(t, window_size))
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def get_upper(self, t, window_size=1):
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def get_upper(self, *args):
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sets, t, window_size = self.unpack_args(*args)
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if len(self.RHS) > 0:
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if isinstance(self.RHS, (list, set)):
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return max([r.get_upper(common.window_index(t, window_size)) for r in self.RHS])
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return max([sets[r].get_upper(common.window_index(t, window_size)) for r in self.RHS])
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elif isinstance(self.RHS, dict):
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return max([self.RHS[r].get_upper(common.window_index(t, window_size)) for r in self.RHS.keys()])
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return max([sets[r].get_upper(common.window_index(t, window_size)) for r in self.RHS.keys()])
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else:
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return self.LHS[-1].get_upper(common.window_index(t, window_size))
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return sets[self.LHS[-1]].get_upper(common.window_index(t, window_size))
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@ -12,87 +12,217 @@ class ConventionalNonStationaryFLRG(flrg.NonStationaryFLRG):
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self.RHS = set()
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def get_key(self):
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return self.LHS.name
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return self.LHS
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def append_rhs(self, c, **kwargs):
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self.RHS.add(c)
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def __str__(self):
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tmp = self.LHS.name + " -> "
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tmp = self.LHS + " -> "
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tmp2 = ""
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for c in sorted(self.RHS, key=lambda s: s.name):
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for c in sorted(self.RHS):
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if len(tmp2) > 0:
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tmp2 = tmp2 + ","
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tmp2 = tmp2 + c.name
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tmp2 = tmp2 + c
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return tmp + tmp2
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class NonStationaryFTS(fts.FTS):
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"""NonStationaryFTS Fuzzy Time Series"""
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def __init__(self, name, **kwargs):
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super(NonStationaryFTS, self).__init__(1, "NSFTS " + name, **kwargs)
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def __init__(self, **kwargs):
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super(NonStationaryFTS, self).__init__(**kwargs)
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self.name = "Non Stationary FTS"
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self.shortname = "NSFTS"
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self.detail = ""
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self.flrgs = {}
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self.method = kwargs.get('method','conditional')
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self.is_high_order = False
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if self.partitioner is not None:
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self.append_transformation(self.partitioner.transformation)
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if self.method == 'conditional':
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self.min_stack = [0, 0, 0]
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self.max_stack = [0, 0, 0]
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self.uod_clip = False
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self.order = 1
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self.min_order = 1
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self.max_lag = 1
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self.inputs = []
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self.forecasts = []
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self.residuals = []
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self.variance_residual = 0.
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self.mean_residual = 0.
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self.memory_window = kwargs.get("memory_window", 5)
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def generate_flrg(self, flrs, **kwargs):
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for flr in flrs:
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if flr.LHS.name in self.flrgs:
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self.flrgs[flr.LHS.name].append_rhs(flr.RHS)
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self.flrgs[flr.LHS.name].append_rhs(flr.RHS.name)
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else:
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self.flrgs[flr.LHS.name] = ConventionalNonStationaryFLRG(flr.LHS)
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self.flrgs[flr.LHS.name].append_rhs(flr.RHS)
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self.flrgs[flr.LHS.name] = ConventionalNonStationaryFLRG(flr.LHS.name)
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self.flrgs[flr.LHS.name].append_rhs(flr.RHS.name)
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def _smooth(self, a):
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return .1 * a[0] + .3 * a[1] + .6 * a[2]
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def train(self, data, **kwargs):
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window_size = kwargs.get('parameters', 1)
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tmpdata = common.fuzzySeries(data, self.sets, self.partitioner.ordered_sets,
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window_size, method='fuzzy')
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flrs = FLR.generate_recurrent_flrs(tmpdata)
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if self.method == 'unconditional':
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window_size = kwargs.get('parameters', 1)
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tmpdata = common.fuzzySeries(data, self.sets,
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self.partitioner.ordered_sets,
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window_size, method='fuzzy')
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else:
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tmpdata = common.fuzzySeries(data, self.sets,
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self.partitioner.ordered_sets,
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method='fuzzy', const_t=0)
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flrs = FLR.generate_non_recurrent_flrs(tmpdata)
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self.generate_flrg(flrs)
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if self.method == 'conditional':
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self.forecasts = self.forecast(data, no_update=True)
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self.residuals = np.array(data[1:]) - np.array(self.forecasts[:-1])
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self.variance_residual = np.var(self.residuals) # np.max(self.residuals
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self.mean_residual = np.mean(self.residuals)
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self.residuals = self.residuals[-self.memory_window:].tolist()
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self.forecasts = self.forecasts[-self.memory_window:]
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self.inputs = np.array(data[-self.memory_window:]).tolist()
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def conditional_perturbation_factors(self, data, **kwargs):
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npart = len(self.partitioner.sets)
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_max = 0
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_min = 0
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if data < self.original_min:
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_min = data - self.original_min if data < 0 else self.original_min - data
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elif data > self.original_max:
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_max = data - self.original_max if data > 0 else self.original_max - data
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self.min_stack.pop(2)
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self.min_stack.insert(0, _min)
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_min = min(self.min_stack)
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self.max_stack.pop(2)
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self.max_stack.insert(0, _max)
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_max = max(self.max_stack)
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_range = (_max - _min)/2
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translate = np.linspace(_min, _max, npart)
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var = np.std(self.residuals)
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var = 0 if var < 1 else var
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loc = (self.mean_residual + np.mean(self.residuals))
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location = [_range + w + loc + k for k in np.linspace(-var,var, npart) for w in translate]
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scale = [abs(location[0] - location[2])]
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scale.extend([abs(location[k - 1] - location[k + 1]) for k in np.arange(1, npart)])
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scale.append(abs(location[-1] - location[-3]))
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perturb = [[location[k], scale[k]] for k in np.arange(npart)]
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return perturb
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def _fsset_key(self, ix):
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return self.partitioner.ordered_sets[ix]
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def _affected_sets(self, sample, perturb):
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if self.method == 'conditional':
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affected_sets = [[ct, self.sets[self._fsset_key(ct)].membership(sample, perturb[ct])]
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for ct in np.arange(len(self.partitioner.sets))
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if self.sets[self._fsset_key(ct)].membership(sample, perturb[ct]) > 0.0]
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if len(affected_sets) == 0:
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if sample < self.partitioner.lower_set().get_lower(perturb[0]):
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affected_sets.append([0, 1])
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elif sample > self.partitioner.upper_set().get_upper(perturb[-1]):
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affected_sets.append([len(self.sets) - 1, 1])
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else:
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affected_sets = [[ct, self.sets[self._fsset_key(ct)].membership(sample, perturb)]
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for ct in np.arange(len(self.partitioner.sets))
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if self.sets[self._fsset_key(ct)].membership(sample, perturb) > 0.0]
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if len(affected_sets) == 0:
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if sample < self.partitioner.lower_set().get_lower(perturb):
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affected_sets.append([0, 1])
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elif sample > self.partitioner.upper_set().get_upper(perturb):
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affected_sets.append([len(self.sets) - 1, 1])
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return affected_sets
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def forecast(self, ndata, **kwargs):
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time_displacement = kwargs.get("time_displacement",0)
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||||
window_size = kwargs.get("window_size", 1)
|
||||
|
||||
no_update = kwargs.get("no_update", False)
|
||||
|
||||
l = len(ndata)
|
||||
|
||||
ret = []
|
||||
|
||||
for k in np.arange(0, l):
|
||||
|
||||
tdisp = common.window_index(k + time_displacement, window_size)
|
||||
sample = ndata[k]
|
||||
|
||||
affected_sets = [ [self.sets[key], self.sets[key].membership(ndata[k], tdisp)]
|
||||
for key in self.partitioner.ordered_sets
|
||||
if self.sets[key].membership(ndata[k], tdisp) > 0.0]
|
||||
if self.method == 'unconditional':
|
||||
perturb = common.window_index(k + time_displacement, window_size)
|
||||
elif self.method == 'conditional':
|
||||
if not no_update:
|
||||
perturb = self.conditional_perturbation_factors(sample)
|
||||
else:
|
||||
perturb = [[0, 1] for k in np.arange(len(self.partitioner.sets))]
|
||||
|
||||
if len(affected_sets) == 0:
|
||||
affected_sets.append([common.check_bounds(ndata[k], self.partitioner, tdisp), 1.0])
|
||||
affected_sets = self._affected_sets(sample, perturb)
|
||||
|
||||
tmp = []
|
||||
numerator = []
|
||||
denominator = []
|
||||
|
||||
if len(affected_sets) == 1:
|
||||
aset = affected_sets[0][0]
|
||||
if aset.name in self.flrgs:
|
||||
tmp.append(self.flrgs[aset.name].get_midpoint(tdisp))
|
||||
ix = affected_sets[0][0]
|
||||
aset = self.partitioner.ordered_sets[ix]
|
||||
if aset in self.flrgs:
|
||||
numerator.append(self.flrgs[aset].get_midpoint(self.sets, perturb[ix]))
|
||||
else:
|
||||
tmp.append(aset.get_midpoint(tdisp))
|
||||
fuzzy_set = self.sets[aset]
|
||||
numerator.append(fuzzy_set.get_midpoint(perturb[ix]))
|
||||
denominator.append(1)
|
||||
else:
|
||||
for aset in affected_sets:
|
||||
if aset[0].name in self.flrgs:
|
||||
tmp.append(self.flrgs[aset[0].name].get_midpoint(tdisp) * aset[1])
|
||||
ix = aset[0]
|
||||
fs = self.partitioner.ordered_sets[ix]
|
||||
tdisp = perturb[ix]
|
||||
if fs in self.flrgs:
|
||||
numerator.append(self.flrgs[fs].get_midpoint(self.sets, tdisp) * aset[1])
|
||||
else:
|
||||
tmp.append(aset[0].get_midpoint(tdisp) * aset[1])
|
||||
fuzzy_set = self.sets[fs]
|
||||
numerator.append(fuzzy_set.get_midpoint(tdisp) * aset[1])
|
||||
denominator.append(aset[1])
|
||||
|
||||
pto = sum(tmp)
|
||||
|
||||
#print(pto)
|
||||
if sum(denominator) > 0:
|
||||
pto = sum(numerator) / sum(denominator)
|
||||
else:
|
||||
pto = sum(numerator)
|
||||
|
||||
ret.append(pto)
|
||||
|
||||
if self.method == 'conditional' and not no_update:
|
||||
self.forecasts.append(pto)
|
||||
self.residuals.append(self.inputs[-1] - self.forecasts[-1])
|
||||
self.inputs.append(sample)
|
||||
|
||||
self.inputs.pop(0)
|
||||
self.forecasts.pop(0)
|
||||
self.residuals.pop(0)
|
||||
|
||||
return ret
|
||||
|
||||
def forecast_interval(self, ndata, **kwargs):
|
||||
|
||||
@ -56,7 +56,7 @@ class PolynomialNonStationaryPartitioner(partitioner.Partitioner):
|
||||
|
||||
def get_polynomial_perturbations(self, data, **kwargs):
|
||||
w = kwargs.get("window_size", int(len(data) / 5))
|
||||
deg = kwargs.get("degree", 2)
|
||||
degree = kwargs.get("degree", 2)
|
||||
xmax = [data[0]]
|
||||
tmax = [0]
|
||||
xmin = [data[0]]
|
||||
@ -73,17 +73,17 @@ class PolynomialNonStationaryPartitioner(partitioner.Partitioner):
|
||||
xmin.append(tn)
|
||||
tmin.append(np.ravel(np.argwhere(data == tn)).tolist()[0])
|
||||
|
||||
cmax = np.polyfit(tmax, xmax, deg=deg)
|
||||
cmin = np.polyfit(tmin, xmin, deg=deg)
|
||||
cmax = np.polyfit(tmax, xmax, deg=degree)
|
||||
cmin = np.polyfit(tmin, xmin, deg=degree)
|
||||
|
||||
cmed = []
|
||||
|
||||
for d in np.arange(0, deg + 1):
|
||||
for d in np.arange(0, degree + 1):
|
||||
cmed.append(np.linspace(cmin[d], cmax[d], self.partitions)[1:self.partitions - 1])
|
||||
|
||||
loc_params = [cmin.tolist()]
|
||||
for i in np.arange(0, self.partitions - 2):
|
||||
tmp = [cmed[k][i] for k in np.arange(0, deg + 1)]
|
||||
tmp = [cmed[k][i] for k in np.arange(0, degree + 1)]
|
||||
loc_params.append(tmp)
|
||||
loc_params.append(cmax.tolist())
|
||||
|
||||
@ -92,13 +92,13 @@ class PolynomialNonStationaryPartitioner(partitioner.Partitioner):
|
||||
clen = []
|
||||
|
||||
for i in np.arange(1, self.partitions-1):
|
||||
tmp = self.poly_width(loc_params[i - 1], loc_params[i + 1], rng, deg)
|
||||
tmp = self.poly_width(loc_params[i - 1], loc_params[i + 1], rng, degree)
|
||||
clen.append(tmp)
|
||||
|
||||
tmp = self.poly_width(loc_params[0], loc_params[1], rng, deg)
|
||||
tmp = self.poly_width(loc_params[0], loc_params[1], rng, degree)
|
||||
clen.insert(0, tmp)
|
||||
|
||||
tmp = self.poly_width(loc_params[self.partitions-2], loc_params[self.partitions-1], rng, deg)
|
||||
tmp = self.poly_width(loc_params[self.partitions-2], loc_params[self.partitions-1], rng, degree)
|
||||
clen.append(tmp)
|
||||
|
||||
tmp = (loc_params, clen)
|
||||
|
||||
@ -11,17 +11,17 @@ import pandas as pd
|
||||
from pyFTS.data import TAIEX, NASDAQ, SP500, artificial
|
||||
|
||||
datasets = {
|
||||
#"TAIEX": TAIEX.get_data()[:4000],
|
||||
"TAIEX": TAIEX.get_data()[:4000],
|
||||
"SP500": SP500.get_data()[10000:14000],
|
||||
#"NASDAQ": NASDAQ.get_data()[:4000],
|
||||
"NASDAQ": NASDAQ.get_data()[:4000],
|
||||
# Incremental Mean and Incremental Variance
|
||||
#"IMIV": artificial.generate_gaussian_linear(1,0.2,0.2,0.05,it=100, num=40),
|
||||
"IMIV": artificial.generate_gaussian_linear(1,0.2,0.2,0.05,it=100, num=40),
|
||||
# Incremental Mean and Incremental Variance, lower bound equals to 0
|
||||
#"IMIV0": artificial.generate_gaussian_linear(1,0.2,0.,0.05, vmin=0,it=100, num=40),
|
||||
"IMIV0": artificial.generate_gaussian_linear(1,0.2,0.,0.05, vmin=0,it=100, num=40),
|
||||
# Constant Mean and Incremental Variance
|
||||
#"CMIV": artificial.generate_gaussian_linear(5,0.1,0,0.02,it=100, num=40),
|
||||
"CMIV": artificial.generate_gaussian_linear(5,0.1,0,0.02,it=100, num=40),
|
||||
# Incremental Mean and Constant Variance
|
||||
#"IMCV": artificial.generate_gaussian_linear(1,0.6,0.1,0,it=100, num=40)
|
||||
"IMCV": artificial.generate_gaussian_linear(1,0.6,0.1,0,it=100, num=40)
|
||||
}
|
||||
|
||||
train_split = 2000
|
||||
@ -34,9 +34,9 @@ tdiff = Transformations.Differential(1)
|
||||
boxcox = Transformations.BoxCox(0)
|
||||
|
||||
transformations = {
|
||||
#'None': None,
|
||||
'None': None,
|
||||
'Differential(1)': tdiff,
|
||||
#'BoxCox(0)': boxcox
|
||||
'BoxCox(0)': boxcox
|
||||
}
|
||||
|
||||
from pyFTS.partitioners import Grid, Util as pUtil
|
||||
@ -74,14 +74,28 @@ for ds in datasets.keys():
|
||||
progress=False, type='point',
|
||||
file="nsfts_benchmarks.db", dataset=ds, tag=tag)
|
||||
'''
|
||||
|
||||
train_split = 200
|
||||
test_split = 2000
|
||||
for ds in datasets.keys():
|
||||
dataset = datasets[ds]
|
||||
|
||||
print(ds)
|
||||
|
||||
for tf in transformations.keys():
|
||||
for tf in ['None']: #transformations.keys():
|
||||
transformation = transformations[tf]
|
||||
train = dataset[:train_split]
|
||||
test = dataset[train_split:test_split]
|
||||
|
||||
fs = nspart.simplenonstationary_gridpartitioner_builder(data=train, npart=partitions[ds][tf], transformation=transformation)
|
||||
print(fs)
|
||||
#cvfts1 = cvfts.ConditionalVarianceFTS(partitioner=fs)
|
||||
model = nsfts.NonStationaryFTS(partitioner=fs)
|
||||
model.fit(train)
|
||||
print(model)
|
||||
|
||||
forecasts = model.predict(test)
|
||||
'''
|
||||
#print(forecasts)
|
||||
|
||||
partitioning = partitions[ds][tf]
|
||||
|
||||
@ -93,3 +107,4 @@ for ds in datasets.keys():
|
||||
partitions=[partitioning],
|
||||
progress=False, type='point',
|
||||
file="nsfts_benchmarks.db", dataset=ds, tag=tag)
|
||||
'''
|
||||
|
||||
Loading…
Reference in New Issue
Block a user