def test_basic_arma():
arma = ARIMA(order=(0, 0, 0), suppress_warnings=True)
preds = arma.fit_predict(y) # fit/predict for coverage
# No OOB, so assert none
assert arma.oob_preds_ is None
# test some of the attrs
assert_almost_equal(arma.aic(), 11.201, decimal=3) # equivalent in R
# intercept is param 0
intercept = arma.params()[0]
assert_almost_equal(intercept, 0.441, decimal=3) # equivalent in R
assert_almost_equal(arma.aicc(), 11.74676, decimal=5)
assert_almost_equal(arma.bic(), 13.639060053303311, decimal=5)
# get predictions
expected_preds = np.array([
0.44079876, 0.44079876, 0.44079876, 0.44079876, 0.44079876, 0.44079876,
0.44079876, 0.44079876, 0.44079876, 0.44079876
])
# generate predictions
assert_array_almost_equal(preds, expected_preds)
# Make sure we can get confidence intervals
expected_intervals = np.array([[-0.10692387, 0.98852139],
[-0.10692387, 0.98852139],
[-0.10692387, 0.98852139],
[-0.10692387, 0.98852139],
[-0.10692387, 0.98852139],
[-0.10692387, 0.98852139],
[-0.10692387, 0.98852139],
[-0.10692387, 0.98852139],
[-0.10692387, 0.98852139],
[-0.10692387, 0.98852139]])
_, intervals = arma.predict(n_periods=10, return_conf_int=True, alpha=0.05)
assert_array_almost_equal(intervals, expected_intervals)
class Model:
def select_data(self):
# Merge columns into a single dataframe of observed values, based on date
dataset = files.data_main.join(files.data_exo.set_index('date'),
on='date').dropna()
# Select part of the precipitation dataframe that corresponds to the forecast
obs_end = dataset.tail(1)['date'].values[0]
exo_prev = files.data_exo[(files.data_exo['date'] > obs_end)]
# Select predict dates
self.dates_prev = exo_prev['date']
# Reshape
endo_obs = np.array(dataset['endo_value'])
self.endo_obs = endo_obs.reshape(-1, 1)
exo_obs = np.array(dataset['exo_value'])
self.exo_obs = exo_obs.reshape(-1, 1)
exo_prev = np.array(exo_prev['exo_value'])
self.exo_prev = exo_prev.reshape(-1, 1)
def normalize(self):
# Calculate lambda only if doesn't have zero values
n_zeros = len(self.endo_obs[self.endo_obs <= 0])
if n_zeros == 0:
self.endo_obs2, self.lambda_boxcox = boxcox(self.endo_obs)
else:
self.lambda_boxcox = -999
# Limit lambda values
if abs(self.lambda_boxcox[0]) > 1:
self.endo_obs2 = self.endo_obs
self.lambda_boxcox = -999
#print(self.endo_obs2, self.lambda_boxcox)
def run_auto(self):
self.arima_model = auto_arima(self.endo_obs2,
start_p=0,
start_d=0,
start_q=0,
max_p=3,
max_d=1,
max_q=3,
start_P=0,
start_Q=0,
D=1,
seasonal=False,
m=1,
exogeneous=self.exo_obs,
trace=True,
error_action='ignore',
suppress_warnings=True,
stepwise=True)
#print(model.arima_model.summary())
# Compile parameters to list
self.parameters = [
self.arima_model.order, self.arima_model.seasonal_order,
self.lambda_boxcox[0],
self.arima_model.aic()
]
print(self.parameters)
return (self.arima_model)
def run_auto_arimax(self):
lower_aic = float(99999)
best_pdq = [0, 0, 0]
param = list(itertools.product(range(0, 4), range(0, 2), range(0, 4)))
for pdq in param:
#print(pdq)
try:
self.arima_model = ARIMA(order=pdq,
suppress_warnings=True).fit(
y=self.endo_obs2,
exogenous=self.exo_obs)
if self.arima_model.aic() < lower_aic:
lower_aic = self.arima_model.aic()
best_pdq = tuple(self.arima_model.order)
except:
continue
#print(model.arima_model.summary())
# Compile parameters to list
self.parameters = [best_pdq, self.lambda_boxcox[0], lower_aic]
print(self.parameters)
return (self.arima_model)
def run_auto_sarimax(self):
lower_aic = float(99999)
best_pdq = [0, 0, 0]
best_spdq = [0, 0, 0]
param = list(itertools.product(range(0, 4), range(0, 2), range(0, 4)))
m = 1 # frequency
param_seasonal = [(x[0], x[1], x[2], m) for x in list(
itertools.product(range(0, 4), range(0, 2), range(0, 4)))]
for pdq in param:
for spdq in param_seasonal:
try:
mod = sm.tsa.statespace.SARIMAX(
self.endo_obs2,
exog=self.exo_obs,
order=pdq,
seasonal_order=spdq,
enforce_stationarity=False,
enforce_invertibility=False)
self.arima_model = mod.fit(disp=0)
print('ARIMA{}x{}{} - AIC:{}'.format(
pdq, spdq, m, self.arima_model.aic))
if self.arima_model.aic() < lower_aic:
lower_aic = self.arima_model.aic()
best_pdq = tuple(pdq)
best_spdq = tuple(spdq)
except:
continue
#print(model.arima_model.summary())
# Compile parameters to list
self.parameters = [
best_pdq, best_spdq, self.lambda_boxcox[0], lower_aic
]
print(self.parameters)
return (self.arima_model)
def forecast(self):
#self.predict = self.arima_model.predict(n_periods=self.exo_prev.shape[0], exogenous=self.exo_prev)
self.predict = self.arima_model.predict(
n_periods=self.exo_prev.shape[0],
exogenous=self.exo_prev,
return_conf_int=True,
alpha=0.7)
def renormalize(self):
if self.lambda_boxcox == float(-999):
self.predict_mean = self.predict[0]
self.predict_down = self.predict[1][:, 0]
self.predict_up = self.predict[1][:, 1]
else:
self.predict_mean = inv_boxcox(self.predict[0], self.lambda_boxcox)
self.predict_down = inv_boxcox(self.predict[1][:, 0],
self.lambda_boxcox)
self.predict_up = inv_boxcox(self.predict[1][:, 1],
self.lambda_boxcox)
# Join predict dates with values into a dataframe
df_final = pd.DataFrame(self.predict_mean, self.dates_prev)
df_final.columns = ['endo_value']
return (df_final)
