def main():
fts = sFTS(do_plots=False)
for dataset_name in dataset_names:
dataset = get_dataset(dataset_name)
bchmk.sliding_window_benchmarks(
dataset,
1000,
train=0.5,
inc=0.2,
benchmark_models=True,
benchmark_methods=benchmark_methods,
benchmark_methods_parameters=benchmark_methods_parameters,
models=[fts],
build_methods=False,
transformations=[None],
orders=[1, 2, 3],
partitions=[35], # np.arange(10,100,2),
progress=False,
type='point',
steps_ahead=[1],
#distributed=True, nodes=['192.168.0.110', '192.168.0.107','192.168.0.106'],\n",
file="benchmarks.db",
dataset=dataset_name,
tag='incremental')
'''
model = knn.KNearestNeighbors(order=3)
#model = ensemble.AllMethodEnsembleFTS("", partitioner=partitioner)
#model = arima.ARIMA("", order=(2,0,2))
#model = quantreg.QuantileRegression("", order=2, dist=True)
#model.append_transformation(tdiff)
model.fit(dataset[:800])
print(Measures.get_distribution_statistics(dataset[800:1000], model))
#tmp = model.predict(dataset[800:1000], type='distribution')
#for tmp2 in tmp:
# print(tmp2)
#'''
'''
bchmk.sliding_window_benchmarks(dataset, 1000, train=0.8, inc=0.2,
methods=[hofts.HighOrderFTS], #[pwfts.ProbabilisticWeightedFTS],
benchmark_models=False,
transformations=[None],
orders=[1, 2, 3],
partitions=np.arange(30, 80, 5),
progress=False, type="point",
#steps_ahead=[1,2,4,6,8,10],
distributed=True, nodes=['192.168.0.110', '192.168.0.107', '192.168.0.106'],
file="benchmarks.db", dataset="TAIEX", tag="comparisons")
bchmk.sliding_window_benchmarks(dataset, 1000, train=0.8, inc=0.2,
print(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,
transformations=[None],
#orders=[1, 2, 3],
partitions=np.arange(10, 100, 2),
progress=False,
type="point",
#steps_ahead=[1,2,4,6,8,10],
distributed=False,
nodes=['192.168.0.110', '192.168.0.107', '192.168.0.106'],
file="benchmarks.db",
dataset="TAIEX",
tag="comparisons")
bchmk.sliding_window_benchmarks(
dataset,
1000,
train=0.8,
inc=0.2,
methods=[chen.ConventionalFTS], # [pwfts.ProbabilisticWeightedFTS],
from pyFTS.models import pwfts, song, ifts, hofts
from pyFTS.models.ensemble import ensemble
'''
model = knn.KNearestNeighbors(order=3)
#model = ensemble.AllMethodEnsembleFTS("", partitioner=partitioner)
#model = arima.ARIMA("", order=(2,0,2))
#model = quantreg.QuantileRegression("", order=2, dist=True)
#model.append_transformation(tdiff)
model.fit(dataset[:800])
print(Measures.get_distribution_statistics(dataset[800:1000], model))
#tmp = model.predict(dataset[800:1000], type='distribution')
#for tmp2 in tmp:
# print(tmp2)
#'''
'''
bchmk.sliding_window_benchmarks(dataset, 1000, train=0.8, inc=0.2,
methods=[hofts.HighOrderFTS], #[pwfts.ProbabilisticWeightedFTS],
benchmark_models=False,
transformations=[None],
orders=[1, 2, 3],
partitions=np.arange(30, 80, 5),
progress=False, type="point",
#steps_ahead=[1,2,4,6,8,10],
distributed=True, nodes=['192.168.0.110', '192.168.0.107', '192.168.0.106'],
file="benchmarks.db", dataset="NASDAQ", tag="comparisons")
bchmk.sliding_window_benchmarks(dataset, 1000, train=0.8, inc=0.2,