Source code for pyFTS.models.cheng
"""
Trend Weighted Fuzzy Time Series by Cheng, Chen and Wu (2009)
C.-H. Cheng, Y.-S. Chen, and Y.-L. Wu, “Forecasting innovation diffusion of products using trend-weighted fuzzy time-series model,”
Expert Syst. Appl., vol. 36, no. 2, pp. 1826–1832, 2009.
"""
import numpy as np
from pyFTS.common import FuzzySet, FLR, fts
from pyFTS.models import yu
[docs]class TrendWeightedFLRG(yu.WeightedFLRG):
"""
First Order Trend Weighted Fuzzy Logical Relationship Group
"""
def __init__(self, LHS, **kwargs):
super(TrendWeightedFLRG, self).__init__(LHS, **kwargs)
self.w = None
[docs] def weights(self, sets):
if self.w is None:
count_nochange = 0.0
count_up = 0.0
count_down = 0.0
weights = []
for c in self.RHS:
tmp = 0
if sets[self.LHS].centroid == sets[c].centroid:
count_nochange += 1.0
tmp = count_nochange
elif sets[self.LHS].centroid > sets[c].centroid:
count_down += 1.0
tmp = count_down
else:
count_up += 1.0
tmp = count_up
weights.append(tmp)
tot = sum(weights)
self.w = np.array([k / tot for k in weights])
return self.w
[docs]class TrendWeightedFTS(yu.WeightedFTS):
"""First Order Trend Weighted Fuzzy Time Series"""
def __init__(self, **kwargs):
super(TrendWeightedFTS, self).__init__(**kwargs)
self.shortname = "TWFTS"
self.name = "Trend Weighted FTS"
self.detail = "Cheng"
self.is_high_order = False
[docs] def generate_FLRG(self, flrs):
for flr in flrs:
if flr.LHS in self.flrgs:
self.flrgs[flr.LHS].append_rhs(flr.RHS)
else:
self.flrgs[flr.LHS] = TrendWeightedFLRG(flr.LHS)
self.flrgs[flr.LHS].append_rhs(flr.RHS)