from pyFTS.data import TAIEX as tx
from pyFTS.common import Transformations
from pyFTS.data import SONDA
df = SONDA.get_dataframe()
train = df.iloc[0:578241] #three years
test = df.iloc[1572480:2096640] #one year
del df
from pyFTS.partitioners import Grid, Util as pUtil
from pyFTS.common import Transformations, Util
from pyFTS.models.multivariate import common, variable, mvfts
from pyFTS.models.seasonal import partitioner as seasonal
from pyFTS.models.seasonal.common import DateTime
bc = Transformations.BoxCox(0)
tdiff = Transformations.Differential(1)
np = 10
model = mvfts.MVFTS("")
fig, axes = plt.subplots(nrows=5, ncols=1, figsize=[15, 10])
sp = {
'seasonality':
DateTime.day_of_year,
'names': [
'Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct',
'Nov', 'Dec'
]
import matplotlib.pylab as plt import pandas as pd from pyFTS.common import Util as cUtil, FuzzySet from pyFTS.partitioners import Grid, Entropy, Util as pUtil, Simple from pyFTS.benchmarks import benchmarks as bchmk, Measures from pyFTS.models import chen, yu, cheng, ismailefendi, hofts, pwfts, tsaur, song, sadaei, ifts from pyFTS.models.ensemble import ensemble from pyFTS.common import Membership, Util from pyFTS.benchmarks import arima, quantreg, BSTS, gaussianproc, knn from pyFTS.common import Transformations tdiff = Transformations.Differential(1) boxcox = Transformations.BoxCox(0) from pyFTS.data import Enrollments, AirPassengers ''' data = AirPassengers.get_data() roi = Transformations.ROI() #plt.plot(data) _roi = roi.apply(data) #plt.plot(_roi) plt.plot(roi.inverse(_roi, data)) '''