def pftsExploreOrderAndPartitions(data, save=False, file=None):
fig, axes = plt.subplots(nrows=4, ncols=1, figsize=[6, 8])
data_fs1 = Grid.GridPartitioner(data=data, npart=10).sets
mi = []
ma = []
axes[0].set_title('Point Forecasts by Order')
axes[2].set_title('Interval Forecasts by Order')
for order in np.arange(1, 6):
fts = pwfts.ProbabilisticWeightedFTS("")
fts.shortname = "n = " + str(order)
fts.train(data, sets=data_fs1.sets, order=order)
point_forecasts = fts.forecast(data)
interval_forecasts = fts.forecast_interval(data)
lower = [kk[0] for kk in interval_forecasts]
upper = [kk[1] for kk in interval_forecasts]
mi.append(min(lower) * 0.95)
ma.append(max(upper) * 1.05)
for k in np.arange(0, order):
point_forecasts.insert(0, None)
lower.insert(0, None)
upper.insert(0, None)
axes[0].plot(point_forecasts, label=fts.shortname)
axes[2].plot(lower, label=fts.shortname)
axes[2].plot(upper)
axes[1].set_title('Point Forecasts by Number of Partitions')
axes[3].set_title('Interval Forecasts by Number of Partitions')
for partitions in np.arange(5, 11):
data_fs = Grid.GridPartitioner(data=data, npart=partitions).sets
fts = pwfts.ProbabilisticWeightedFTS("")
fts.shortname = "q = " + str(partitions)
fts.train(data, sets=data_fs.sets, order=1)
point_forecasts = fts.forecast(data)
interval_forecasts = fts.forecast_interval(data)
lower = [kk[0] for kk in interval_forecasts]
upper = [kk[1] for kk in interval_forecasts]
mi.append(min(lower) * 0.95)
ma.append(max(upper) * 1.05)
point_forecasts.insert(0, None)
lower.insert(0, None)
upper.insert(0, None)
axes[1].plot(point_forecasts, label=fts.shortname)
axes[3].plot(lower, label=fts.shortname)
axes[3].plot(upper)
for ax in axes:
ax.set_ylabel('F(T)')
ax.set_xlabel('T')
ax.plot(data, label="Original", color="black", linewidth=1.5)
handles, labels = ax.get_legend_handles_labels()
ax.legend(handles, labels, loc=2, bbox_to_anchor=(1, 1))
ax.set_ylim([min(mi), max(ma)])
ax.set_xlim([0, len(data)])
plt.tight_layout()
cUtil.show_and_save_image(fig, file, save)
#df = pd.read_csv('https://query.data.world/s/z2xo3t32pkl4mdzp63x6lyne53obmi')
#dados = df.iloc[2710:2960 , 0:1].values # somente a 1 coluna sera usada
#dados = df['temperature'].values
#dados = dados.flatten().tolist()
dados = Enrollments.get_data()
l = len(dados)
#dados_treino = dados[:int(l*.7)]
#dados_teste = dados[int(l*.7):]
particionador = Grid.GridPartitioner(data = dados, npart = 10, func = Membership.trimf)
modelo = pwfts.ProbabilisticWeightedFTS(partitioner = particionador, order = 1, standard_horizon=3)
#modelo = hofts.WeightedHighOrderFTS(partitioner = particionador, order = 1, standard_horizon=2)
#modelo = chen.ConventionalFTS(partitioner = particionador, standard_horizon=3)
modelo.fit(dados)
print(modelo)
# Todo o procedimento de inferência é feito pelo método predict
predicoes = modelo.predict(dados)
print(predicoes)
'''
from pyFTS.data import TAIEX, NASDAQ, SP500
ttr = list(range(len(dados_treino)))
ordem = 1 # ordem do modelo, indica quantos ultimos valores serao usados
dados_teste = dados[qtde_dt_tr - ordem:250]
tts = list(
range(
len(dados_treino) - ordem,
len(dados_treino) + len(dados_teste) - ordem))
particionador = Grid.GridPartitioner(data=dados_treino,
npart=30,
func=Membership.trimf)
modelo = pwfts.ProbabilisticWeightedFTS(partitioner=particionador, order=ordem)
modelo.fit(dados_treino)
print(modelo)
# Todo o procedimento de inferência é feito pelo método predict
predicoes = modelo.predict(dados_teste[38:40])
print(predicoes)
'''
from pyFTS.data import TAIEX, NASDAQ, SP500
from pyFTS.common import Util
train = TAIEX.get_data()[1000:1800]
test = TAIEX.get_data()[1800:2000]
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]) handles, labels = ax.get_legend_handles_labels() lgd = ax.legend(handles, labels, loc=2, bbox_to_anchor=(1, 1)) df = pd.DataFrame(rows, columns=['Algorithms','RMSE','MAPE','U']) print(df)
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
from pyFTS.common import Util
from pyFTS.benchmarks import benchmarks as bchmk, Measures
from pyFTS.models import pwfts, hofts, ifts
from pyFTS.models.multivariate import granular, grid
from pyFTS.partitioners import Grid, Util as pUtil
from pyFTS.models.multivariate import common, variable, mvfts, wmvfts
from pyFTS.models.seasonal import partitioner as seasonal
from pyFTS.models.seasonal.common import DateTime
from pyFTS.common import Membership
from pyFTS.data import SONDA
data = [
k for k in SONDA.get_data('ws_10m')
if k > 0.1 and k != np.nan and k is not None
]
data = [np.nanmean(data[k:k + 60]) for k in np.arange(0, len(data), 60)]
train = data[:9000]
test = data[9000:10000]
fs = Grid.GridPartitioner(data=train, npart=95)
model = pwfts.ProbabilisticWeightedFTS(partitioner=fs, order=3)
model.fit(train)
model.predict(test)
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)
print(model.shortname)
print(Measures.get_point_statistics(test, model))