def hofts_forecast(train_df, test_df, params):
_partitioner = params['partitioner']
_npartitions = params['npartitions']
_order = params['order']
_input = params['input']
_step = params.get('step', 1)
fuzzy_sets = _partitioner(data=train_df[_input].values, npart=_npartitions)
model = hofts.HighOrderFTS(order=_order)
model.fit(train_df[_input].values, order=_order, partitioner=fuzzy_sets)
forecast = model.predict(test_df[_input].values, steps_ahead=_step)
return forecast
y = [np.sin(k) for k in x]
from pyFTS.models import hofts
from pyFTS.partitioners import Grid, FCM, CMeans, Entropy
from pyFTS.benchmarks import Measures
metodos = [FCM.FCMPartitioner]
k = 35
rows = []
for contador, metodo in enumerate(metodos):
print(metodo)
part = metodo(data=y, npart=k)
model = hofts.HighOrderFTS(order=2, partitioner=part)
model.fit(y)
forecasts = model.predict(y)
for o in range(model.order):
forecasts.insert(0, None)
'''
model = fts.FCM_FTS(partitioner=fs, order=1)
model.fcm.weights = np.array([
[1, 1, 0, -1, -1],
[1, 1, 1, 0, -1],
[0, 1, 1, 1, 0],
[-1, 0, 1, 1, 1],
[-1, -1, 0, 1, 1]
])
taiex = TAIEX.get_data()
train = taiex[:3000]
test = taiex[3000:3200]
from pyFTS.common import Transformations
tdiff = Transformations.Differential(1)
from pyFTS.benchmarks import benchmarks as bchmk, Measures
from pyFTS.models import pwfts, hofts, ifts
from pyFTS.partitioners import Grid, Util as pUtil
fs = Grid.GridPartitioner(data=train, npart=30) #, transformation=tdiff)
model1 = hofts.HighOrderFTS(partitioner=fs, lags=[1, 2]) #lags=[0,1])
model1.shortname = "1"
model2 = pwfts.ProbabilisticWeightedFTS(partitioner=fs, lags=[1, 2])
#model2.append_transformation(tdiff)
model2.shortname = "2"
#model = pwfts.ProbabilisticWeightedFTS(partitioner=fs, order=2)# lags=[1,2])
model1.fit(train)
model2.fit(train)
#print(model1)
#print(model2)
for model in [model1, model2]:
#forecasts = model.predict(test)
from pyFTS.partitioners import Grid, Util as pUtil
partitioner = Grid.GridPartitioner(data=dataset[:2000],
npart=20) #, transformation=tdiff)
from pyFTS.common import Util as cUtil
from pyFTS.benchmarks import benchmarks as bchmk, Util as bUtil, Measures, knn, quantreg, arima, naive
from pyFTS.models import pwfts, song, chen, ifts, hofts
from pyFTS.models.ensemble import ensemble
print(partitioner)
#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")
'''
model = knn.KNearestNeighbors(order=3)
#model = ensemble.AllMethodEnsembleFTS("", partitioner=partitioner)
test = stock[init_os:end_os]
close = variable.Variable("close",
data_label='Adj Close',
partitioner=Grid.GridPartitioner,
npart=20,
data=train)
polarity = variable.Variable("polarity",
data_label='sentiment_bert',
partitioner=Grid.GridPartitioner,
npart=50,
data=train)
from pyFTS.models import hofts
#mpolarity = mvfts.MVFTS(explanatory_variables=[close, polarity], target_variable=polarity)
mpolarity = hofts.HighOrderFTS(partitioner=polarity.partitioner)
mpolarity.fit(train['sentiment_bert'].values)
mclose = mvfts.MVFTS(explanatory_variables=[close, polarity],
target_variable=close)
mclose.fit(train)
forecasts = mclose.predict(train[-1:],
steps_ahead=2,
generators={'sentiment_bert': mpolarity})
'''
df = Malaysia.get_dataframe()
df['time'] = pd.to_datetime(df["time"], format='%m/%d/%y %I:%M %p')
train_mv = df.iloc[:1800]
test_mv = df.iloc[1800:2000]