def cluster_method(individual, dataset, **kwargs):
from pyFTS.common import Util, Membership
from pyFTS.models import hofts
from pyFTS.partitioners import Grid, Entropy
from pyFTS.benchmarks import Measures
import numpy as np
if individual['mf'] == 1:
mf = Membership.trimf
elif individual['mf'] == 2:
mf = Membership.trapmf
elif individual['mf'] == 3 and individual['partitioner'] != 2:
mf = Membership.gaussmf
else:
mf = Membership.trimf
window_size = kwargs.get('window_size', 800)
train_rate = kwargs.get('train_rate', .8)
increment_rate = kwargs.get('increment_rate', .2)
parameters = kwargs.get('parameters', {})
errors = []
sizes = []
for count, train, test in Util.sliding_window(dataset,
window_size,
train=train_rate,
inc=increment_rate):
if individual['partitioner'] == 1:
partitioner = Grid.GridPartitioner(data=train,
npart=individual['npart'],
func=mf)
elif individual['partitioner'] == 2:
npart = individual['npart'] if individual['npart'] > 10 else 10
partitioner = Entropy.EntropyPartitioner(data=train,
npart=npart,
func=mf)
model = hofts.WeightedHighOrderFTS(partitioner=partitioner,
lags=individual['lags'],
alpha_cut=individual['alpha'],
order=individual['order'])
model.fit(train)
forecasts = model.predict(test)
#rmse, mape, u = Measures.get_point_statistics(test, model)
rmse = Measures.rmse(test[model.max_lag:], forecasts)
size = len(model)
errors.append(rmse)
sizes.append(size)
return {
'parameters': individual,
'rmse': np.nanmean(errors),
'size': np.nanmean(size)
}
def cluster_method(individual, train, test):
from pyFTS.common import Util, Membership
from pyFTS.models import hofts
from pyFTS.partitioners import Grid, Entropy
from pyFTS.benchmarks import Measures
if individual['mf'] == 1:
mf = Membership.trimf
elif individual['mf'] == 2:
mf = Membership.trapmf
elif individual['mf'] == 3 and individual['partitioner'] != 2:
mf = Membership.gaussmf
else:
mf = Membership.trimf
if individual['partitioner'] == 1:
partitioner = Grid.GridPartitioner(data=train, npart=individual['npart'], func=mf)
elif individual['partitioner'] == 2:
npart = individual['npart'] if individual['npart'] > 10 else 10
partitioner = Entropy.EntropyPartitioner(data=train, npart=npart, func=mf)
model = hofts.WeightedHighOrderFTS(partitioner=partitioner,
lags=individual['lags'],
alpha_cut=individual['alpha'],
order=individual['order'])
model.fit(train)
rmse, mape, u = Measures.get_point_statistics(test, model)
size = len(model)
return individual, rmse, size, mape, u
def phenotype(individual, train):
try:
if individual['mf'] == 1:
mf = Membership.trimf
elif individual['mf'] == 2:
mf = Membership.trapmf
elif individual['mf'] == 3 and individual['partitioner'] != 2:
mf = Membership.gaussmf
else:
mf = Membership.trimf
if individual['partitioner'] == 1:
partitioner = Grid.GridPartitioner(data=train,
npart=individual['npart'],
func=mf)
elif individual['partitioner'] == 2:
partitioner = Entropy.EntropyPartitioner(data=train,
npart=individual['npart'],
func=mf)
model = hofts.WeightedHighOrderFTS(partitioner=partitioner,
lags=individual['lags'],
alpha_cut=individual['alpha'],
order=individual['order'])
model.fit(train)
return model
except Exception as ex:
print("EXCEPTION!", str(ex), str(individual))
return None
from pyFTS.partitioners import Grid, Entropy, Util as pUtil
from pyFTS.benchmarks import benchmarks as bchmk, Measures
from pyFTS.models import chen, yu, cheng, ismailefendi, hofts, pwfts
from pyFTS.common import Transformations
tdiff = Transformations.Differential(1)
from pyFTS.data import TAIEX, SP500, NASDAQ, Malaysia
dataset = Malaysia.get_data('temperature')[:1000]
p = Grid.GridPartitioner(data=dataset, npart=20)
print(p)
model = hofts.WeightedHighOrderFTS(partitioner=p, order=2)
model.fit(dataset) #[22, 22, 23, 23, 24])
print(model)
Measures.get_point_statistics(dataset, model)
'''
#dataset = SP500.get_data()[11500:16000]
#dataset = NASDAQ.get_data()
#print(len(dataset))
bchmk.sliding_window_benchmarks(dataset, 1000, train=0.8, inc=0.2,
methods=[chen.ConventionalFTS], #[pwfts.ProbabilisticWeightedFTS],
benchmark_models=False,
rows = [] fig, ax = plt.subplots(nrows=1, ncols=1,figsize=[15,7]) ax.plot(y, label="Original Data", color="Black") part = Grid.GridPartitioner(data=y, npart=80) model = hofts.HighOrderFTS(order=2, partitioner=part) model.fit(y) forecasts = model.predict(y) plt_fc = np.insert(forecasts, 0, y[0]) ax.plot(plt_fc, label="HOFTS") rmse, mape, u = Measures.get_point_statistics(y, model) rows.append(["HOFTS", rmse, mape, u]) model = hofts.WeightedHighOrderFTS(order=2, partitioner=part) model.fit(y) forecasts = model.predict(y) plt_fc = np.insert(forecasts, 0, y[0]) ax.plot(plt_fc, label="WHOFTS") rmse, mape, u = Measures.get_point_statistics(y, model) rows.append(["WHOFTS", rmse, mape, u]) model = pwfts.ProbabilisticWeightedFTS(order=2, partitioner=part) model.fit(y) forecasts = model.predict(y) plt_fc = np.insert(forecasts, 0, y[0]) ax.plot(plt_fc, label="PWFTS") rmse, mape, u = Measures.get_point_statistics(y, model) rows.append(["PWFTS", rmse, mape, u])
from pyFTS.models.incremental import IncrementalEnsemble, TimeVariant
from pyFTS.data import AirPassengers, artificial
from pyFTS.models.ensemble import ensemble
from pyFTS.models import hofts
from pyFTS.data import TAIEX
data = TAIEX.get_data()
model = ensemble.EnsembleFTS()
for k in [15, 25, 35]:
for order in [1, 2]:
fs = Grid.GridPartitioner(data=data, npart=k)
tmp = hofts.WeightedHighOrderFTS(partitioner=fs)
tmp.fit(data)
model.append_model(tmp)
forecasts = model.predict(data, type='interval', method='quantile', alpha=.05)
from pyFTS.benchmarks import benchmarks as bchmk
#f, ax = plt.subplots(1, 1, figsize=[20, 5])
#ax.plot(data)
#bchmk.plot_interval(ax, forecasts, 3, "")
print(forecasts)
'''