156 lines
4.1 KiB
Python
156 lines
4.1 KiB
Python
#!/usr/bin/python
|
|
# -*- coding: utf8 -*-
|
|
|
|
"""Residual Analysis methods"""
|
|
|
|
import numpy as np
|
|
import pandas as pd
|
|
import matplotlib as plt
|
|
import matplotlib.pyplot as plt
|
|
from pyFTS.common import Transformations,Util
|
|
from pyFTS.benchmarks import Measures
|
|
from scipy import stats
|
|
|
|
|
|
def residuals(targets, forecasts, order=1):
|
|
"""First order residuals"""
|
|
return np.array(targets[order:]) - np.array(forecasts[:-1])
|
|
|
|
|
|
def chi_squared(q, h):
|
|
"""
|
|
Chi-Squared value
|
|
:param q:
|
|
:param h:
|
|
:return:
|
|
"""
|
|
p = stats.chi2.sf(q, h)
|
|
return p
|
|
|
|
|
|
def compare_residuals(data, models):
|
|
"""
|
|
Compare residual's statistics of several models
|
|
:param data:
|
|
:param models:
|
|
:return:
|
|
"""
|
|
ret = "Model & Order & Mean & STD & Box-Pierce & Box-Ljung & P-value \\\\ \n"
|
|
for mfts in models:
|
|
forecasts = mfts.forecast(data)
|
|
res = residuals(data, forecasts, mfts.order)
|
|
mu = np.mean(res)
|
|
sig = np.std(res)
|
|
ret += mfts.shortname + " & "
|
|
ret += str(mfts.order) + " & "
|
|
ret += str(round(mu,2)) + " & "
|
|
ret += str(round(sig,2)) + " & "
|
|
q1 = Measures.BoxPierceStatistic(res, 10)
|
|
ret += str(round(q1,2)) + " & "
|
|
q2 = Measures.BoxLjungStatistic(res, 10)
|
|
ret += str(round(q2,2)) + " & "
|
|
ret += str(chi_squared(q2, 10))
|
|
ret += " \\\\ \n"
|
|
return ret
|
|
|
|
|
|
def plotResiduals(targets, models, tam=[8, 8], save=False, file=None):
|
|
"""
|
|
Plot residuals and statistics
|
|
:param targets:
|
|
:param models:
|
|
:param tam:
|
|
:param save:
|
|
:param file:
|
|
:return:
|
|
"""
|
|
fig, axes = plt.subplots(nrows=len(models), ncols=3, figsize=tam)
|
|
for c, mfts in enumerate(models):
|
|
if len(models) > 1:
|
|
ax = axes[c]
|
|
else:
|
|
ax = axes
|
|
forecasts = mfts.forecast(targets)
|
|
res = residuals(targets,forecasts,mfts.order)
|
|
mu = np.mean(res)
|
|
sig = np.std(res)
|
|
|
|
ax[0].set_title("Residuals Mean=" + str(mu) + " STD = " + str(sig))
|
|
ax[0].set_ylabel('E')
|
|
ax[0].set_xlabel('T')
|
|
ax[0].plot(res)
|
|
|
|
ax[1].set_title("Residuals Autocorrelation")
|
|
ax[1].set_ylabel('ACS')
|
|
ax[1].set_xlabel('Lag')
|
|
ax[1].acorr(res)
|
|
|
|
ax[2].set_title("Residuals Histogram")
|
|
ax[2].set_ylabel('Freq')
|
|
ax[2].set_xlabel('Bins')
|
|
ax[2].hist(res)
|
|
|
|
c += 1
|
|
|
|
plt.tight_layout()
|
|
|
|
Util.show_and_save_image(fig, file, save)
|
|
|
|
|
|
def plot_residuals(targets, models, tam=[8, 8], save=False, file=None):
|
|
fig, axes = plt.subplots(nrows=len(models), ncols=3, figsize=tam)
|
|
|
|
for c, mfts in enumerate(models, start=0):
|
|
if len(models) > 1:
|
|
ax = axes[c]
|
|
else:
|
|
ax = axes
|
|
forecasts = mfts.forecast(targets)
|
|
res = residuals(targets, forecasts, mfts.order)
|
|
mu = np.mean(res)
|
|
sig = np.std(res)
|
|
|
|
if c == 0: ax[0].set_title("Residuals", size='large')
|
|
ax[0].set_ylabel(mfts.shortname, size='large')
|
|
ax[0].set_xlabel(' ')
|
|
ax[0].plot(res)
|
|
|
|
if c == 0: ax[1].set_title("Residuals Autocorrelation", size='large')
|
|
ax[1].set_ylabel('ACS')
|
|
ax[1].set_xlabel('Lag')
|
|
ax[1].acorr(res)
|
|
|
|
if c == 0: ax[2].set_title("Residuals Histogram", size='large')
|
|
ax[2].set_ylabel('Freq')
|
|
ax[2].set_xlabel('Bins')
|
|
ax[2].hist(res)
|
|
|
|
plt.tight_layout()
|
|
|
|
Util.show_and_save_image(fig, file, save)
|
|
|
|
|
|
def single_plot_residuals(targets, forecasts, order, tam=[8, 8], save=False, file=None):
|
|
fig, ax = plt.subplots(nrows=1, ncols=3, figsize=tam)
|
|
|
|
res = residuals(targets, forecasts, order)
|
|
|
|
ax[0].set_title("Residuals", size='large')
|
|
ax[0].set_ylabel("Model", size='large')
|
|
ax[0].set_xlabel(' ')
|
|
ax[0].plot(res)
|
|
|
|
ax[1].set_title("Residuals Autocorrelation", size='large')
|
|
ax[1].set_ylabel('ACS')
|
|
ax[1].set_xlabel('Lag')
|
|
ax[1].acorr(res)
|
|
|
|
ax[2].set_title("Residuals Histogram", size='large')
|
|
ax[2].set_ylabel('Freq')
|
|
ax[2].set_xlabel('Bins')
|
|
ax[2].hist(res)
|
|
|
|
plt.tight_layout()
|
|
|
|
Util.show_and_save_image(fig, file, save)
|