119 lines
2.8 KiB
Python
119 lines
2.8 KiB
Python
import numpy as np
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import math
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from pyFTS import *
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class Transformation(object):
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"""
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Data transformation used to pre and post processing of the FTS
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"""
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def __init__(self, parameters):
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self.isInversible = True
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self.parameters = parameters
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self.minimalLength = 1
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def apply(self,data,param):
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pass
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def inverse(self,data, param):
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pass
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def __str__(self):
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return self.__class__.__name__ + '(' + str(self.parameters) + ')'
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class Differential(Transformation):
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"""
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Differentiation data transform
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"""
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def __init__(self, parameters):
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super(Differential, self).__init__(parameters)
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self.lag = parameters
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self.minimalLength = 2
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def apply(self, data, param=None):
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if param is not None:
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self.lag = param
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if not isinstance(data, (list, np.ndarray, np.generic)):
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data = [data]
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if isinstance(data, (np.ndarray, np.generic)):
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data = data.tolist()
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n = len(data)
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diff = [data[t - self.lag] - data[t] for t in np.arange(self.lag, n)]
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for t in np.arange(0, self.lag): diff.insert(0, 0)
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return diff
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def inverse(self,data, param):
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if isinstance(data, (np.ndarray, np.generic)):
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data = data.tolist()
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if not isinstance(data, list):
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data = [data]
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n = len(data)
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inc = [data[t] + param[t] for t in np.arange(0, n)]
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if n == 1:
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return inc[0]
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else:
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return inc
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class AdaptiveExpectation(Transformation):
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"""
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Adaptive Expectation post processing
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"""
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def __init__(self, parameters):
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super(AdaptiveExpectation, self).__init__(parameters)
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self.h = parameters
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def apply(self, data, param=None):
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return data
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def inverse(self, data, param):
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n = len(data)
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inc = [param[t] + self.h*(data[t] - param[t]) for t in np.arange(0, n)]
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if n == 1:
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return inc[0]
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else:
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return inc
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def boxcox(original, plambda):
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n = len(original)
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if plambda != 0:
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modified = [(original[t] ** plambda - 1) / plambda for t in np.arange(0, n)]
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else:
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modified = [math.log(original[t]) for t in np.arange(0, n)]
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return np.array(modified)
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def Z(original):
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mu = np.mean(original)
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sigma = np.std(original)
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z = [(k - mu)/sigma for k in original]
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return z
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# retrieved from Sadaei and Lee (2014) - Multilayer Stock ForecastingModel Using Fuzzy Time Series
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def roi(original):
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n = len(original)
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roi = []
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for t in np.arange(0, n-1):
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roi.append( (original[t+1] - original[t])/original[t] )
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return roi
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def smoothing(original, lags):
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pass
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def aggregate(original, operation):
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pass
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