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Improvements on ResidualAnalysis

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Petrônio Cândido 2018-05-25 09:40:25 -03:00
parent b058ed5daa
commit a2002c20b1
1 changed files with 9 additions and 13 deletions
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22
pyFTS/benchmarks/ResidualAnalysis.py
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@ -33,25 +33,21 @@ def compare_residuals(data, models):
Compare residual's statistics of several models
:param data: test data
:param models:
:return:
:return: a Pandas dataframe with the Box-Ljung statistic for each model
"""
ret = "Model & Order & Mean & STD & Box-Pierce & Box-Ljung & P-value \\\\ \n"
from statsmodels.stats.diagnostic import acorr_ljungbox
rows = []
columns = ["Model","Order","AVG","STD","Box-Ljung","p-value"]
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
row = [mfts.shortname, mfts.order, mu, sig]
stat, pval = acorr_ljungbox(res)
row.extend([stat, pval])
rows.append(row)
return pd.DataFrame(rows, columns=columns)
def plotResiduals(targets, models, tam=[8, 8], save=False, file=None):