def test_ar_select_order():
# GH#2118
np.random.seed(12345)
y = sm.tsa.arma_generate_sample([1, -.75, .3], [1], 100)
ts = Series(y, index=date_range(start='1/1/1990', periods=100, freq='M'))
ar = AR(ts)
res = ar.select_order(maxlag=12, ic='aic')
assert_(res == 2)
def test_ar_select_order():
# 2118
np.random.seed(12345)
y = sm.tsa.arma_generate_sample([1, -0.75, 0.3], [1], 100)
ts = Series(y, index=DatetimeIndex(start="1/1/1990", periods=100, freq="M"))
ar = AR(ts)
res = ar.select_order(maxlag=12, ic="aic")
assert_(res == 2)
def test_ar_select_order_tstat():
rs = np.random.RandomState(123)
tau = 25
y = rs.randn(tau)
ts = Series(y, index=date_range(start='1/1/1990', periods=tau, freq='M'))
ar = AR(ts)
res = ar.select_order(maxlag=5, ic='t-stat')
assert_equal(res, 0)
def test_ar_select_order():
# 2118
np.random.seed(12345)
y = sm.tsa.arma_generate_sample([1, -.75, .3], [1], 100)
ts = TimeSeries(y, index=DatetimeIndex(start='1/1/1990', periods=100,
freq='M'))
ar = AR(ts)
res = ar.select_order(maxlag=12, ic='aic')
assert_(res == 2)
def test_ar_select_order_tstat():
rs = np.random.RandomState(123)
tau = 25
y = rs.randn(tau)
ts = Series(y, index=date_range(start="1/1/1990", periods=tau, freq="M"))
with pytest.warns(FutureWarning):
ar = AR(ts)
with pytest.warns(FutureWarning):
res = ar.select_order(maxlag=5, ic="t-stat")
assert_equal(res, 0)
def test_ar_select_order():
# GH#2118
np.random.seed(12345)
y = sm.tsa.arma_generate_sample([1, -0.75, 0.3], [1], 100)
ts = Series(y, index=date_range(start="1/1/1990", periods=100, freq="M"))
with pytest.warns(FutureWarning):
ar = AR(ts)
with pytest.warns(FutureWarning):
res = ar.select_order(maxlag=12, ic="aic")
assert res == 2
def test_ar_select_order_tstat():
rs = np.random.RandomState(123)
tau = 25
y = rs.randn(tau)
ts = Series(y, index=date_range(start='1/1/1990', periods=tau,
freq='M'))
ar = AR(ts)
res = ar.select_order(maxlag=5, ic='t-stat')
assert_equal(res, 0)
def AR_model(log_returns, tickers_with_AR):
print("AR_model() start execute")
tickers = tickers_with_AR
#log_returns = to_log_return(data)
summary = {}
for ticker in tickers:
print("AR_model() start execute in ticker: " + ticker)
log_rtn = log_returns[ticker].dropna()
model = AR(log_rtn)
best_order = model.select_order(maxlag=LAGS, ic='aic')
result = model.fit(best_order)
aic = result.aic
summary[ticker] = [best_order, aic, 'AR']
with open('results/AR_models.txt', 'w') as results_file:
for k in summary.keys():
message = "Ticker: {} \t\t\tBest order: {} \tAIC = {}\n".format(
k, summary[k][0], summary[k][1])
results_file.write(message)
return summary
class VarModel(BaseModel):
def __init__(self, feat_id, run_id, data=None):
self.model_type = 'VAR'
self.opt_p = 1
super().__init__(feat_id, run_id, data)
def train(self, data):
if len(data.columns) > 1:
self.model = VAR(data)
self.opt_p = self.model.select_order(30).aic
else:
self.model = AR(data)
self.opt_p = self.model.select_order(30, 'aic')
def save(self):
joblib.dump(
self.opt_p,
os.path.join(
'temp', self.run_id, 'models', 'VAR', '{}_VAR.pkl'.format(
replace_multiple(
self.feat_id,
['/', '\\', ':', '?', '*', '"', '<', '>', '|'], "x"))))
def load(self):
self.opt_p = \
joblib.load(os.path.join('runs', self.run_id, 'models', 'VAR',
'{}_VAR.pkl'.format(replace_multiple(self.feat_id,
['/', '\\',
':', '?',
'*', '"',
'<', '>',
'|'],
"x"))))
def result(self, history, actual, prediction, forecast, anomaly_scores):
mse = mean_squared_error(actual, prediction)
mae = mean_absolute_error(actual, prediction)
rmse = np.sqrt(mse)
anomaly_scores['points'] = anomaly_scores.index
future_alert = anomaly_scores.tail(len(forecast))
past_alert = anomaly_scores.iloc[:len(history)]
future_alert = future_alert[future_alert['outlier'] == -1]
past_alert = past_alert[past_alert['outlier'] == -1]
output = {
'history': history.tolist(),
'expected': prediction.tolist(),
'forecast': forecast.tolist(),
'rmse': rmse,
'mse': mse,
'mae': mae,
'future_alerts': future_alert.fillna(0).to_dict(orient='record'),
'past_alerts': past_alert.fillna(0).to_dict(orient='record'),
'model': self.model_type
}
return output
def predict(self, data, start_idx, end_idx):
if len(data.columns) > 1:
self.model = VAR(data)
result = self.model.fit(self.opt_p)
y_pred = self.model.predict(result.params,
start=start_idx,
end=end_idx,
lags=self.opt_p)
return pd.DataFrame(data=y_pred, columns=data.columns.values)
else:
self.model = AR(data)
self.model = self.model.fit(self.opt_p)
y_pred = self.model.predict(start=start_idx, end=end_idx)
return pd.DataFrame(data=y_pred, columns=data.columns.values)
def orderSelection(a):
model = AR(a)
maxlag = 28
order = model.select_order(maxlag=maxlag, ic='aic', trend='nc')
return order
def test_ar_select_order_tstat():
rs = np.random.RandomState(123)
tau = 25
y = rs.randn(tau)
ts = Series(y, index=DatetimeIndex(start="1/1/1990", periods=tau, freq="M"))
ar = AR(ts)
res = ar.select_order(maxlag=5, ic="t-stat")
assert_equal(res, 0)
