AnalyticsCal-Online/arimaMA.py at master · AnalyticsCal/AnalyticsCal-Online · GitHub

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import numpy as np
import lmfit as lm
import itertools

def __init__(self,y,acf,q,index):
        self.index = 0
        self.q = 1

def fitterfn(params, x1, x2, x3, data):
        a = params['a']
        b = params['b']
        k = params['k']

        model = [k * k for k in x1] + [a * i for i in x2] + [b * i for i in x3]
        # x1 - x2 for (x1, x2) in zip(List1, List2)
        return [k1 - k2 for (k1, k2) in zip(model, data)]


def getthetas(y, et):
        x1 = et
        x2 = et[1:]
        x3 = et[2:]
        finalthetas = []
        params = lm.Parameters()
        params.add('a', value=0, min=-1, max=1)
        params.add('b', value=0, min=-1, max=1)
        params.add('k', value=0, min=-1, max=1)
        result = lm.minimize(fitterfn, params, args=(x1, x2, x3, y))
        finalthetas.append(result.params.get('a').value)
        finalthetas.append(result.params.get('b').value)
        finalthetas.append(result.params.get('k').value)
        return finalthetas


def prelimthetas(acf, q):
        p = []  # co-eff array
        prelimtheta = []

        theta = []
        p.append([1 - acf[1], 1, -acf[1]])
        eliminations = []
        x1arr = np.ndarray.tolist(np.roots(p[0]))
        for i in x1arr:
            if not isinstance(i, complex):
                if i > 1 or i < -1:
                    eliminations.append(i)
            else:
                eliminations.append(i)
        theta.append([x for x in x1arr if x not in eliminations])
        if q == 2:
            eliminations = []
            x2arr = []
            for k in theta[0]:
                p.append([acf[1] + acf[2], 1 - 2 * k, acf[1] + acf[2] + (acf[1] + acf[2]) * k ** 2 + k])
                x2arr = np.ndarray.tolist(np.roots(p.pop()))
                for i in x2arr:
                    if not isinstance(i, complex):
                        if i > 1 or i < -1:
                            eliminations.append(i)
                    else:
                        eliminations.append(i)
            theta.append([x for x in x1arr if x not in eliminations])
        else:
            print("q>2 not supported")
        if len(theta) > 1:
            prelimtheta = list(itertools.product(theta[0], theta[1]))
        else:
            prelimtheta = theta[0]
        return prelimtheta


def getMaCoeff(y, acf, q, index):
        prelimtheta = prelimthetas(acf, q)
        et = []
        if len(y) < 10:
            print("Input time series not suitable for forecasting")
            return

        # print(prelimtheta)
        for k in range(len(prelimtheta)):
            if isinstance(prelimtheta[k], float):
                list1 = [0, y[0], y[1] + prelimtheta[k] * y[0]]
            else:
                list1 = [0, y[0], y[1] + prelimtheta[k][0] * y[0],
                         y[2] + prelimtheta[k][1] * y[1] + prelimtheta[k][1] * prelimtheta[k][0] * y[0]]
            et.append(list1)
        finalthetas = []
        for k in range(len(et)):
            finalthetas.append(getthetas(y, et[k]))
        if index > len(finalthetas):
            index = 0
        #print(finalthetas[index][0:q])
        return finalthetas[index][0:q]