pyFTS/benchmarks/ResidualAnalysis.py

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2017-01-20 05:24:44 +04:00
#!/usr/bin/python
# -*- coding: utf8 -*-
import numpy as np
import pandas as pd
import matplotlib as plt
import matplotlib.pyplot as plt
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from pyFTS.common import Transformations,Util
from pyFTS.benchmarks import Measures
from scipy import stats
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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()
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Util.showAndSaveImage(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):
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)