def arima_model(vEndog, mExog=None, tPDQ=None):
"""
Fits an ARIMA model. Order can be specified or determined by auto_arima.
Differently from other models, it does not work on patsy/R formula syntax.
:param vEndog: DataFrame column/numpy vector containing endogenous data (which will be regressed upon itself)
:param mExog: vector/matrix containing exogenous data. Defaults to None
:param tPDQ: tuple (p, d, q) containing order of the model;
p: number of autorregressions (AR)
q: number of differentiations (I)
q: number of past prevision errors/moving averages (MA)
If None (default), performs an auto_arima()
:return mod: fitted model instance
"""
## Creating model
# If order is specified
if tPDQ is not None:
# Conditional on whether there are exogenous variables
if mExog is None:
mod_arima = ARIMA(endog=vEndog, order=tPDQ).fit(cov_type='robust')
else:
mod_arima = ARIMA(endog=vEndog, exog=mExog, order=tPDQ).fit(cov_type='robust')
# If order isn't specified, use auto_arima()
else:
mod_arima = auto_arima(y=vEndog, X=mExog)
mod_arima = mod_arima.fit(y=vEndog, cov_type='robust')
## Printing summary and diagnostics
print(mod_arima.summary())
print("For heteroskdasticity, check Prob(H), where H0: homoskedasticity, and the standardized residual graph.")
print("If there is hetero., the model error can't be a white noise (which is the desired thing).")
print("Estimaed Density and Jarque-Bera have information on normality.")
print("In the correlogram, all lollipops must be inside of the shaded area.")
# Plots
mod_arima.plot_diagnostics(figsize=(10, 10))
plt.show()
# Residual means
tMean0 = stats.ttest_1samp(mod_arima.resid(), 0, nan_policy='omit')
print(f"P-value for the test that residual mean is equal to 0: {np.around(tMean0[1], 5)}.")
print("If p < 0.05, H0 is rejected and the residual mean is different from 0 (not ideal).")
## Returning
return mod_arima
def arima(
other_args: List[str],
stock: pd.Series,
):
"""Arima model
Parameters
----------
other_args : str
Command line arguments to be processed with argparse
stock : pd.Series
Stock data
Returns
----------
model_name : str
Command line arguments to be processed with argparse
model : pd.Series
Model series
residuals : List[float]
Residuals from model fitting stock data
"""
parser = argparse.ArgumentParser(
add_help=False,
prog="arima",
description="""
ARIMA model
""",
)
parser.add_argument(
"-i",
"--ic",
action="store",
dest="s_ic",
type=str,
default="aic",
choices=["aic", "aicc", "bic", "hqic", "oob"],
help="information criteria.",
)
parser.add_argument(
"-s",
"--seasonal",
action="store_true",
default=False,
dest="b_seasonal",
help="Use weekly seasonal data.",
)
parser.add_argument(
"-o",
"--order",
action="store",
dest="s_order",
type=str,
help="arima model order (p,d,q) in format: pdq.",
)
try:
ns_parser = parse_known_args_and_warn(parser, other_args)
if not ns_parser:
return "", None, list()
if ns_parser.s_order:
t_order = tuple(int(ord) for ord in list(ns_parser.s_order))
model_fit = ARIMA(stock.values, order=t_order).fit()
model = pd.Series(model_fit.fittedvalues[1:],
index=stock.index[1:])
model_name = f"ARIMA {t_order}"
residuals = model_fit.resid
else:
if ns_parser.b_seasonal:
model_fit = pmdarima.auto_arima(
stock.values,
error_action="ignore",
seasonal=True,
m=5,
information_criteria=ns_parser.s_ic,
)
else:
model_fit = pmdarima.auto_arima(
stock.values,
error_action="ignore",
seasonal=False,
information_criteria=ns_parser.s_ic,
)
model = pd.Series(model_fit.predict_in_sample()[1:],
index=stock.index[1:])
model_name = f"ARIMA {model_fit.order}"
residuals = model_fit.resid()
return model_name, model, residuals
except Exception as e:
print(e, "\n")
return "", None, list()
