77 lines
2.2 KiB
Python
77 lines
2.2 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 statsmodels.tsa.arima_model import ARMA
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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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ndata = np.array(self.doTransformations(data))
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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(ndata, 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 ar(self, data):
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return data.dot(self.model_fit.arparams)
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def ma(self, data):
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return data.dot(self.model_fit.maparams)
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def forecast(self, data):
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if self.model_fit is None:
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return np.nan
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order = self.p
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ndata = np.array(self.doTransformations(data))
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l = len(ndata)
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ret = []
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ar = np.array([self.ar(ndata[k - self.p: k]) for k in np.arange(self.p, l)])
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residuals = np.array([ar[k - self.p] - ndata[k] for k in np.arange(self.p, l)])
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ma = np.array([self.ma(residuals[k - self.q : k]) for k in np.arange(self.q, len(ar)+1)])
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ret = ar + ma
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ret = self.doInverseTransformations(ret, params=[data[order - 1:]])
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return ret
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