5c8c80cd8d
- statsmodels ARIMA wrapper for benchmarks - method refactoring at PWFTS - auto_update at PWFTS - method refactoring at ResidualAnalysis
52 lines
1.7 KiB
Python
52 lines
1.7 KiB
Python
#!/usr/bin/python
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# -*- coding: utf8 -*-
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import numpy as np
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from statsmodels.tsa.arima_model import ARIMA as stats_arima
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from pyFTS import fts
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class ARIMA(fts.FTS):
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def __init__(self, name):
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super(ARIMA, self).__init__(1, "ARIMA")
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self.name = "ARIMA"
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self.detail = "Auto Regressive Integrated Moving Average"
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self.isHighOrder = True
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self.model = None
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self.model_fit = None
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self.trained_data = None
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self.p = 1
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self.d = 0
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self.q = 0
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self.benchmark_only = True
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self.minOrder = 1
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def train(self, data, sets, order=1, parameters=None):
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if parameters is not None:
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self.p = parameters[0]
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self.d = parameters[1]
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self.q = parameters[2]
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self.order = max([self.p, self.d, self.q])
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self.shortname = "ARIMA(" + str(self.p) + "," + str(self.d) + "," + str(self.q) + ")"
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old_fit = self.model_fit
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self.model = stats_arima(data, order=(self.p, self.d, self.q))
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try:
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self.model_fit = self.model.fit(disp=0)
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except:
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try:
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self.model = stats_arima(data, order=(self.p, self.d, self.q))
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self.model_fit = self.model.fit(disp=1)
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except:
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self.model_fit = old_fit
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self.trained_data = data #.tolist()
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def forecast(self, data):
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ret = []
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for t in data:
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output = self.model_fit.forecast()
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ret.append( output[0] )
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self.trained_data = np.append(self.trained_data, t) #.append(t)
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self.train(self.trained_data,None,order=self.order, parameters=(self.p, self.d, self.q))
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return ret |