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# Holt-Winters algorithms to forecasting | |
# Coded in Python 2 by: Andre Queiroz | |
# Description: This module contains three exponential smoothing algorithms. They are Holt's linear trend method and Holt-Winters seasonal methods (additive and multiplicative). | |
# References: | |
# Hyndman, R. J.; Athanasopoulos, G. (2013) Forecasting: principles and practice. http://otexts.com/fpp/. Accessed on 07/03/2013. | |
# Byrd, R. H.; Lu, P.; Nocedal, J. A Limited Memory Algorithm for Bound Constrained Optimization, (1995), SIAM Journal on Scientific and Statistical Computing, 16, 5, pp. 1190-1208. | |
from sys import exit | |
from math import sqrt | |
from numpy import array | |
from scipy.optimize import fmin_l_bfgs_b | |
def RMSE(params, *args): | |
Y = args[0] | |
type = args[1] | |
rmse = 0 | |
if type == 'linear': | |
alpha, beta = params | |
a = [Y[0]] | |
b = [Y[1] - Y[0]] | |
y = [a[0] + b[0]] | |
for i in range(len(Y)): | |
a.append(alpha * Y[i] + (1 - alpha) * (a[i] + b[i])) | |
b.append(beta * (a[i + 1] - a[i]) + (1 - beta) * b[i]) | |
y.append(a[i + 1] + b[i + 1]) | |
else: | |
alpha, beta, gamma = params | |
m = args[2] | |
a = [sum(Y[0:m]) / float(m)] | |
b = [(sum(Y[m:2 * m]) - sum(Y[0:m])) / m ** 2] | |
if type == 'additive': | |
s = [Y[i] - a[0] for i in range(m)] | |
y = [a[0] + b[0] + s[0]] | |
for i in range(len(Y)): | |
a.append(alpha * (Y[i] - s[i]) + (1 - alpha) * (a[i] + b[i])) | |
b.append(beta * (a[i + 1] - a[i]) + (1 - beta) * b[i]) | |
s.append(gamma * (Y[i] - a[i] - b[i]) + (1 - gamma) * s[i]) | |
y.append(a[i + 1] + b[i + 1] + s[i + 1]) | |
elif type == 'multiplicative': | |
s = [Y[i] / a[0] for i in range(m)] | |
y = [(a[0] + b[0]) * s[0]] | |
for i in range(len(Y)): | |
a.append(alpha * (Y[i] / s[i]) + (1 - alpha) * (a[i] + b[i])) | |
b.append(beta * (a[i + 1] - a[i]) + (1 - beta) * b[i]) | |
s.append(gamma * (Y[i] / (a[i] + b[i])) + (1 - gamma) * s[i]) | |
y.append((a[i + 1] + b[i + 1])* s[i + 1]) | |
else: | |
exit('Type must be either linear, additive or multiplicative') | |
rmse = sqrt(sum([(m - n) ** 2 for m, n in zip(Y, y[:-1])]) / len(Y)) | |
return rmse | |
def linear(x, fc, alpha = None, beta = None): | |
Y = x[:] | |
if (alpha == None or beta == None): | |
initial_values = array([0.3, 0.1]) | |
boundaries = [(0, 1), (0, 1)] | |
type = 'linear' | |
parameters = fmin_l_bfgs_b(RMSE, x0 = initial_values, args = (Y, type), bounds = boundaries, approx_grad = True) | |
alpha, beta = parameters[0] | |
a = [Y[0]] | |
b = [Y[1] - Y[0]] | |
y = [a[0] + b[0]] | |
rmse = 0 | |
for i in range(len(Y) + fc): | |
if i == len(Y): | |
Y.append(a[-1] + b[-1]) | |
a.append(alpha * Y[i] + (1 - alpha) * (a[i] + b[i])) | |
b.append(beta * (a[i + 1] - a[i]) + (1 - beta) * b[i]) | |
y.append(a[i + 1] + b[i + 1]) | |
rmse = sqrt(sum([(m - n) ** 2 for m, n in zip(Y[:-fc], y[:-fc - 1])]) / len(Y[:-fc])) | |
return Y[-fc:], alpha, beta, rmse | |
def additive(x, m, fc, alpha = None, beta = None, gamma = None): | |
Y = x[:] | |
if (alpha == None or beta == None or gamma == None): | |
initial_values = array([0.3, 0.1, 0.1]) | |
boundaries = [(0, 1), (0, 1), (0, 1)] | |
type = 'additive' | |
parameters = fmin_l_bfgs_b(RMSE, x0 = initial_values, args = (Y, type, m), bounds = boundaries, approx_grad = True) | |
alpha, beta, gamma = parameters[0] | |
a = [sum(Y[0:m]) / float(m)] | |
b = [(sum(Y[m:2 * m]) - sum(Y[0:m])) / m ** 2] | |
s = [Y[i] - a[0] for i in range(m)] | |
y = [a[0] + b[0] + s[0]] | |
rmse = 0 | |
for i in range(len(Y) + fc): | |
if i == len(Y): | |
Y.append(a[-1] + b[-1] + s[-m]) | |
a.append(alpha * (Y[i] - s[i]) + (1 - alpha) * (a[i] + b[i])) | |
b.append(beta * (a[i + 1] - a[i]) + (1 - beta) * b[i]) | |
s.append(gamma * (Y[i] - a[i] - b[i]) + (1 - gamma) * s[i]) | |
y.append(a[i + 1] + b[i + 1] + s[i + 1]) | |
rmse = sqrt(sum([(m - n) ** 2 for m, n in zip(Y[:-fc], y[:-fc - 1])]) / len(Y[:-fc])) | |
return Y[-fc:], alpha, beta, gamma, rmse | |
def multiplicative(x, m, fc, alpha = None, beta = None, gamma = None): | |
Y = x[:] | |
if (alpha == None or beta == None or gamma == None): | |
initial_values = array([0.0, 1.0, 0.0]) | |
boundaries = [(0, 1), (0, 1), (0, 1)] | |
type = 'multiplicative' | |
parameters = fmin_l_bfgs_b(RMSE, x0 = initial_values, args = (Y, type, m), bounds = boundaries, approx_grad = True) | |
alpha, beta, gamma = parameters[0] | |
a = [sum(Y[0:m]) / float(m)] | |
b = [(sum(Y[m:2 * m]) - sum(Y[0:m])) / m ** 2] | |
s = [Y[i] / a[0] for i in range(m)] | |
y = [(a[0] + b[0]) * s[0]] | |
rmse = 0 | |
for i in range(len(Y) + fc): | |
if i == len(Y): | |
Y.append((a[-1] + b[-1]) * s[-m]) | |
a.append(alpha * (Y[i] / s[i]) + (1 - alpha) * (a[i] + b[i])) | |
b.append(beta * (a[i + 1] - a[i]) + (1 - beta) * b[i]) | |
s.append(gamma * (Y[i] / (a[i] + b[i])) + (1 - gamma) * s[i]) | |
y.append((a[i + 1] + b[i + 1]) * s[i + 1]) | |
rmse = sqrt(sum([(m - n) ** 2 for m, n in zip(Y[:-fc], y[:-fc - 1])]) / len(Y[:-fc])) | |
return Y[-fc:], alpha, beta, gamma, rmse |
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