pyFTS/benchmarks/ResidualAnalysis.py
Petrônio Cândido de Lima e Silva 8b3aceed58 Cascaded transformations in all fts models
2017-01-27 08:26:47 -02:00

101 lines
3.0 KiB
Python

#!/usr/bin/python
# -*- coding: utf8 -*-
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):
return np.array(targets[order:]) - np.array(forecasts[:-1])
def ChiSquared(q,h):
p = stats.chi2.sf(q, h)
return p
def compareResiduals(data, models):
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(ChiSquared(q2, 10))
ret += " \\\\ \n"
return ret
def plotResiduals(targets, models, tam=[8, 8], save=False, file=None):
fig, axes = plt.subplots(nrows=len(models), ncols=3, figsize=tam)
c = 0
for mfts in models:
forecasts = mfts.forecast(targets)
res = residuals(targets,forecasts,mfts.order)
mu = np.mean(res)
sig = np.std(res)
axes[c][0].set_title("Residuals Mean=" + str(mu) + " STD = " + str(sig))
axes[c][0].set_ylabel('E')
axes[c][0].set_xlabel('T')
axes[c][0].plot(res)
axes[c][1].set_title("Residuals Autocorrelation")
axes[c][1].set_ylabel('ACS')
axes[c][1].set_xlabel('Lag')
axes[c][1].acorr(res)
axes[c][2].set_title("Residuals Histogram")
axes[c][2].set_ylabel('Freq')
axes[c][2].set_xlabel('Bins')
axes[c][2].hist(res)
c += 1
plt.tight_layout()
Util.showAndSaveImage(fig, file, save)
def plotResiduals2(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):
forecasts = mfts.forecast(targets)
res = residuals(targets, forecasts, mfts.order)
mu = np.mean(res)
sig = np.std(res)
if c == 0: axes[c][0].set_title("Residuals", size='large')
axes[c][0].set_ylabel(mfts.shortname, size='large')
axes[c][0].set_xlabel(' ')
axes[c][0].plot(res)
if c == 0: axes[c][1].set_title("Residuals Autocorrelation", size='large')
axes[c][1].set_ylabel('ACS')
axes[c][1].set_xlabel('Lag')
axes[c][1].acorr(res)
if c == 0: axes[c][2].set_title("Residuals Histogram", size='large')
axes[c][2].set_ylabel('Freq')
axes[c][2].set_xlabel('Bins')
axes[c][2].hist(res)
plt.tight_layout()
Util.showAndSaveImage(fig, file, save)