73 lines
2.0 KiB
Python
73 lines
2.0 KiB
Python
#!/usr/bin/python
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# -*- coding: utf8 -*-
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import numpy as np
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import pandas as pd
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import matplotlib as plt
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import matplotlib.pyplot as plt
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from pyFTS.common import Transformations,Util
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from pyFTS.benchmarks import Measures
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from scipy import stats
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def residuals(targets, forecasts, order=1):
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return np.array(targets[order:]) - np.array(forecasts[:-1])
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def ChiSquared(q,h):
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p = stats.chi2.sf(q, h)
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return p
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def compareResiduals(data, models):
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ret = "Model & Order & Mean & STD & Box-Pierce & Box-Ljung & P-value \\\\ \n"
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for mfts in models:
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forecasts = mfts.forecast(data)
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res = residuals(data, forecasts, mfts.order)
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mu = np.mean(res)
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sig = np.std(res)
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ret += mfts.shortname + " & "
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ret += str(mfts.order) + " & "
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ret += str(round(mu,2)) + " & "
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ret += str(round(sig,2)) + " & "
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q1 = Measures.BoxPierceStatistic(res, 10)
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ret += str(round(q1,2)) + " & "
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q2 = Measures.BoxLjungStatistic(res, 10)
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ret += str(round(q2,2)) + " & "
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ret += str(ChiSquared(q2, 10))
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ret += " \\\\ \n"
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return ret
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def plotResiduals(targets, models, tam=[8, 8], save=False, file=None):
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fig, axes = plt.subplots(nrows=len(models), ncols=3, figsize=tam)
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c = 0
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for mfts in models:
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forecasts = mfts.forecast(targets)
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res = residuals(targets,forecasts,mfts.order)
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mu = np.mean(res)
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sig = np.std(res)
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axes[c][0].set_title("Residuals Mean=" + str(mu) + " STD = " + str(sig))
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axes[c][0].set_ylabel('E')
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axes[c][0].set_xlabel('T')
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axes[c][0].plot(res)
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axes[c][1].set_title("Residuals Autocorrelation")
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axes[c][1].set_ylabel('ACS')
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axes[c][1].set_xlabel('Lag')
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axes[c][1].acorr(res)
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axes[c][2].set_title("Residuals Histogram")
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axes[c][2].set_ylabel('Freq')
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axes[c][2].set_xlabel('Bins')
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axes[c][2].hist(res)
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c += 1
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plt.tight_layout()
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Util.showAndSaveImage(fig, file, save)
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