Arima

AutoARIMA

Type

ml-estimator

Class

fire.nodes.ts.NodeAutoARIMA

Fields

Name	Title	Description
y	Y	The time-series to which to fit the ARIMA estimator
default_model	Default Model	Ignore all the parameters and build a default model
exogenous	Exogenous Columns	An optional 2-d array of exogenous variables. If provided, these variables are used as additional features in the regression operation. This should not include a constant or trend.
start_p	start_p	The starting value of p, the order (or number of time lags) of the auto-regressive (“AR”) model. Must be a positive integer.
d	d	The order of first-differencing. If None (by default), the value will automatically be selected based on the results of the test.
start_q	start_q	The starting value of q, the order of the moving-average (“MA”) model. Must be a positive integer.
max_p	max_p	The maximum value of p, inclusive. Must be a positive integer greater than or equal to start_p.
max_d	max_d	The maximum value of d, or the maximum number of non-seasonal differences. Must be a positive integer greater than or equal to d.
max_q	max_q	The maximum value of q, inclusive. Must be a positive integer greater than start_q.
start_P_seasonal	start_P_seasonal	The starting value of P, the order of the auto-regressive portion of the seasonal model.
D_seasonal	D_seasonal	The order of the seasonal differencing. If None (by default, the value will automatically be selected based on the results of the seasonal_test. Must be a positive integer or None.
start_Q_seasonal	start_Q_seasonal	The starting value of Q, the order of the moving-average portion of the seasonal model.
max_P_seasonal	max_P_seasonal	The maximum value of P, inclusive. Must be a positive integer greater than start_P.
max_D_seasonal	max_D_seasonal	The maximum value of D. Must be a positive integer greater than D.
max_Q_seasonal	max_Q_seasonal	The maximum value of Q, inclusive. Must be a positive integer greater than start_Q.
max_order	max_order	If the sum of p and q is >= max_order, a model will not be fit with those parameters, but will progress to the next combination. Default is 5. If max_order is None, it means there are no constraints on maximum order.
m	m	The period for seasonal differencing, m refers to the number of periods in each season.
seasonal	Seasonal	Whether to fit a seasonal ARIMA. Default is True
stationary	Stationary	Whether the time-series is stationary and d should be set to zero.
information_criterion	Information Criterion	The information criterion used to select the best ARIMA model.
alpha	Alpha	Level of the test for testing significance.
test	Test	Type of unit root test to use in order to detect stationarity if stationary is False and d is None. Default is kpss (Kwiatkowski–Phillips–Schmidt–Shin).
seasonal_test	Seasonal Test	This determines which seasonal unit root test is used if seasonal is True and D is None. Default is OCSB.
stepwise	Stepwise	Whether to use the stepwise algorithm to identify the optimal model parameters.
trend	Trend	The trend parameter. If with_intercept is True, trend will be used. If with_intercept is False, the trend will be set to a no- intercept value..
method	Method	The method determines which solver from scipy.optimize is used.
maxiter	Maxiter	The maximum number of function evaluations. Default is 50.
trace	Trace	Whether to print status on the fits.
random	Random	Similar to grid searches, auto_arima provides the capability to perform a “random search” over a hyper-parameter space. If random is True, rather than perform an exhaustive search or stepwise search, only n_fits ARIMA models will be fit (stepwise must be False for this option to do anything).
random_state	Random State	The PRNG for when random=True. Ensures replicable testing and results.
n_fits	N Fits	If random is True and a “random search” is going to be performed, n_iter is the number of ARIMA models to be fit.
out_of_sample_size	Out of Sample Size	The ARIMA class can fit only a portion of the data if specified, in order to retain an “out of bag” sample score. This is the number of examples from the tail of the time series to hold out and use as validation examples. The model will not be fit on these samples, but the observations will be added into the model’s endog and exog arrays so that future forecast values originate from the end of the endogenous vector
scoring	Scoring	The metric to use for scoring the out-of-sample data. One of (mse, mae)
with_intercept	With Intercept	Whether to include an intercept term. Default is “auto” which behaves like True until a point in the search where the sum of differencing terms will explicitly set it to True or False.