Hi All,
I'm currently working on a Holt Winters forecast, however i'm experiencing difficulty in enforcing the trend component I believe? When I try running the script, I receive the message "IndexError: list index out of range". I was wondering if anyone has had any similar problems with enforcing this forecast or encountered the same difficulties? I've been working on this for about 3 months and haven't seemed to make any progress, although I've researched online and I'm 80% sure i'm on the right lines?
Ronnie!
I'm currently working on a Holt Winters forecast, however i'm experiencing difficulty in enforcing the trend component I believe? When I try running the script, I receive the message "IndexError: list index out of range". I was wondering if anyone has had any similar problems with enforcing this forecast or encountered the same difficulties? I've been working on this for about 3 months and haven't seemed to make any progress, although I've researched online and I'm 80% sure i'm on the right lines?
from __future__ import division from sys import exit from math import sqrt from numpy import array from scipy.optimize import fmin_l_bfgs_b import scipy.optimize from datetime import datetime import pandas as pd import matplotlib.pyplot as pylot from scipy import stats import numpy import numpy as np data=pd.read_csv('S:/My Documents/bricks.csv', encoding = "ISO-8859-1", low_memory = False) 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-m]) + (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-m]) y.append(a[i + 1] + b[i + 1] + s[i + 1-m]) 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-m]) + (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-m]) y.append(a[i + 1] + b[i + 1] + s[i + 1-m]) 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 additive(x, m, fc, alpha = None, beta = None, gamma = None): print(fc) 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, factr=10,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] b = [(sum(Y[m:2 * m]) - sum(Y[0:m])) / float(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+1] - s[i+1-m]) + (1 - alpha) * (a[i] + b[i])) b.append(beta * (a[i + 1] - a[i]) + (1 - beta) * b[i]) #correct s.append(gamma * (Y[i+1] - a[i+1] - b[i+1]) + (1 - gamma) * s[i-m+1]) y.append(a[i + 1] + m*b[i + 1] + s[i + 2-m]) rmse = sqrt(sum([(m - n) ** 2 for m, n in zip(Y[:-fc], y[:-fc - 1])]) / len(Y[:-fc])) return Y[-fc:] a=list(data.columns.values) del a[0] iterable=a result=[additive(list(data[x]), 12,12, None, None) for x in iterable] print(result)Many thanks if anyone can help with this Ronnie!