71 lines
1.7 KiB
Python
71 lines
1.7 KiB
Python
import numpy as np
|
|
import math
|
|
from pyFTS import *
|
|
|
|
|
|
class Transformation(object):
|
|
|
|
def __init__(self, parameters):
|
|
self.isInversible = True
|
|
self.parameters = parameters
|
|
|
|
def apply(self,data,param):
|
|
pass
|
|
|
|
def inverse(self,data, param):
|
|
pass
|
|
|
|
def __str__(self):
|
|
return self.__class__.__name__ + '(' + str(self.parameters) + ')'
|
|
|
|
|
|
class Differential(Transformation):
|
|
|
|
def __init__(self, parameters):
|
|
super(Differential, self).__init__(parameters)
|
|
self.lag = parameters
|
|
|
|
def apply(self, data, param=None):
|
|
if param is not None:
|
|
self.lag = param
|
|
n = len(data)
|
|
diff = [data[t - self.lag] - data[t] for t in np.arange(self.lag, n)]
|
|
for t in np.arange(0, self.lag): diff.insert(0, 0)
|
|
return np.array(diff)
|
|
|
|
def inverse(self,data, param):
|
|
n = len(data)
|
|
inc = [data[t] + param[t] for t in np.arange(1, n)]
|
|
return np.array(inc)
|
|
|
|
|
|
def boxcox(original, plambda):
|
|
n = len(original)
|
|
if plambda != 0:
|
|
modified = [(original[t] ** plambda - 1) / plambda for t in np.arange(0, n)]
|
|
else:
|
|
modified = [math.log(original[t]) for t in np.arange(0, n)]
|
|
return np.array(modified)
|
|
|
|
|
|
def Z(original):
|
|
mu = np.mean(original)
|
|
sigma = np.std(original)
|
|
z = [(k - mu)/sigma for k in original]
|
|
return z
|
|
|
|
|
|
# retrieved from Sadaei and Lee (2014) - Multilayer Stock ForecastingModel Using Fuzzy Time Series
|
|
def roi(original):
|
|
n = len(original)
|
|
roi = []
|
|
for t in np.arange(0, n-1):
|
|
roi.append( (original[t+1] - original[t])/original[t] )
|
|
return roi
|
|
|
|
def smoothing(original, lags):
|
|
pass
|
|
|
|
def aggregate(original, operation):
|
|
pass
|