pyFTS/sadaei.py

"""
First Order Exponentialy Weighted Fuzzy Time Series by Sadaei et al. (2013)

H. J. Sadaei, R. Enayatifar, A. H. Abdullah, and A. Gani, “Short-term load forecasting using a hybrid model with a 
refined exponentially weighted fuzzy time series and an improved harmony search,” Int. J. Electr. Power Energy Syst., vol. 62, no. from 2005, pp. 118–129, 2014.
"""

import numpy as np
from pyFTS.common import FuzzySet,FLR
from pyFTS import fts


class ExponentialyWeightedFLRG(object):
    """First Order Exponentialy Weighted Fuzzy Logical Relationship Group"""
    def __init__(self, LHS, c):
        self.LHS = LHS
        self.RHS = []
        self.count = 0.0
        self.c = c

    def append(self, c):
        self.RHS.append(c)
        self.count = self.count + 1.0

    def weights(self):
        wei = [self.c ** k for k in np.arange(0.0, self.count, 1.0)]
        tot = sum(wei)
        return np.array([k / tot for k in wei])

    def __str__(self):
        tmp = self.LHS.name + " -> "
        tmp2 = ""
        cc = 0
        wei = [self.c ** k for k in np.arange(0.0, self.count, 1.0)]
        tot = sum(wei)
        for c in sorted(self.RHS, key=lambda s: s.name):
            if len(tmp2) > 0:
                tmp2 = tmp2 + ","
            tmp2 = tmp2 + c.name + "(" + str(wei[cc] / tot) + ")"
            cc = cc + 1
        return tmp + tmp2

    def __len__(self):
        return len(self.RHS)


class ExponentialyWeightedFTS(fts.FTS):
    """First Order Exponentialy Weighted Fuzzy Time Series"""
    def __init__(self, name, **kwargs):
        super(ExponentialyWeightedFTS, self).__init__(1, "EWFTS")
        self.name = "Exponentialy Weighted FTS"
        self.detail = "Sadaei"
        self.c = 1

    def generateFLRG(self, flrs, c):
        flrgs = {}
        for flr in flrs:
            if flr.LHS.name in flrgs:
                flrgs[flr.LHS.name].append(flr.RHS)
            else:
                flrgs[flr.LHS.name] = ExponentialyWeightedFLRG(flr.LHS, c);
                flrgs[flr.LHS.name].append(flr.RHS)
        return (flrgs)

    def train(self, data, sets,order=1,parameters=2):
        self.c = parameters
        self.sets = sets
        ndata = self.doTransformations(data)
        tmpdata = FuzzySet.fuzzySeries(ndata, sets)
        flrs = FLR.generateRecurrentFLRs(tmpdata)
        self.flrgs = self.generateFLRG(flrs, self.c)

    def forecast(self, data, **kwargs):
        l = 1

        data = np.array(data)

        ndata = self.doTransformations(data)

        l = len(ndata)

        ret = []

        for k in np.arange(0, l):

            mv = FuzzySet.fuzzyInstance(ndata[k], self.sets)

            actual = self.sets[np.argwhere(mv == max(mv))[0, 0]]

            if actual.name not in self.flrgs:
                ret.append(actual.centroid)
            else:
                flrg = self.flrgs[actual.name]
                mp = self.getMidpoints(flrg)

                ret.append(mp.dot(flrg.weights()))

        ret = self.doInverseTransformations(ret, params=[data[self.order - 1:]])

        return ret