def test_non_seasonal_arima1(self):
ts_data = self.getData4()
f_name='non_seasonal_arima1.pmml'
model = ARIMA(ts_data,order=(9, 2, 0))
result = model.fit(trend = 'c', method = 'css-mle')
StatsmodelsToPmml(result, f_name, model_name="arima_920")
self.assertEqual(os.path.isfile(f_name),True)
def test_non_seasonal_arima3(self):
ts_data = self.getData4()
f_name='non_seasonal_arima3.pmml'
model = ARIMA(ts_data,order=(1, 0, 1))
result = model.fit(trend = 'c', method = 'css-mle')
StatsmodelsToPmml(result, f_name, description="A test model")
self.assertEqual(os.path.isfile(f_name),True)
def test_non_seasonal_arima2(self):
ts_data = self.getData4()
f_name = 'non_seasonal_arima2.pmml'
model = ARIMA(ts_data, order=(9, 2, 3))
result = model.fit(trend='nc', method='css-mle')
ArimaToPMML(result, f_name, description="A test model")
self.assertEqual(os.path.isfile(f_name), True)
def test_non_seasonal_arima8(self):
ts_data = self.statsmodels_data_helper.get_non_seasonal_data()
f_name = 'non_seasonal_arima8.pmml'
model = ARIMA(ts_data, order=(5, 1, 2))
result = model.fit(trend='c', method='mle')
ArimaToPMML(result, f_name, conf_int=[80, 95])
self.assertEqual(self.schema.is_valid(f_name), True)
def test_non_seasonal_arima1(self):
ts_data = self.statsmodels_data_helper.get_non_seasonal_data()
f_name = 'non_seasonal_arima1.pmml'
model = ARIMA(ts_data, order=(9, 2, 0))
result = model.fit(trend='c', method='css-mle')
ArimaToPMML(result, f_name)
self.assertEqual(self.schema.is_valid(f_name), True)
def test_non_seasonal_arima7(self):
ts_data = self.statsmodels_data_helper.get_non_seasonal_data()
f_name = 'non_seasonal_arima7.pmml'
model = ARIMA(ts_data, order=(5, 1, 2))
result = model.fit(trend='nc', method='mle')
StatsmodelsToPmml(result, f_name)
self.assertEqual(self.schema.is_valid(f_name), True)
def train(self, array_X, array_Y):
self.train_X = array_X
self.train_Y = array_Y
array = numpy.concatenate((numpy.array([array_Y]).T, array_X), axis=1)
idx = pd.date_range('20130101', periods=48000)
#xxx= pd.DataFrame(data=numpy.array([array_Y]),index=idx)
xxx = pd.DataFrame(data=array, index=idx)
model = ARIMA(endog=xxx, order=(0, 1, 1))
fit = model.fit()
res = fit.fittedvalues.values[:, 0]
res = numpy.hstack((res[0], res))
return res
def predict(self, test_X, test_Y):
predictions = numpy.empty(0)
array_train = numpy.concatenate(
(numpy.array([self.train_Y]).T, self.train_X), axis=1)
array_test = numpy.concatenate((numpy.array([test_Y]).T, test_X),
axis=1)
for t in range(0, test_Y.shape[0]):
array = numpy.vstack((array_train, array_test[:t]))
model = ARIMA(endog=pd.DataFrame(data=array))
fit = model.fit()
lag = fit.k_ar
pred = fit.forecast(array[-lag:], 1)[0]
predictions = numpy.append(predictions, pred[0])
return predictions