smooth.OMG

class smooth.OMG(model_a='MNN', model_b=None, lags=None, orders_a=None, orders_b=None, constant_a=False, constant_b=None, formula_a=None, formula_b=None, regressors_a='use', regressors_b=None, persistence_a=None, persistence_b=None, phi_a=None, phi_b=None, arma_a=None, arma_b=None, h=0, holdout=False, initial='backcasting', loss='likelihood', ic='AICc', bounds='usual', verbose=0, nlopt_kargs=None, ets='conventional')

General occurrence model — two parallel ETS sub-models combined.

Public API:: fit(y, X=None) → self predict(h, X=None) → ForecastResult
Attributes after fit:: model_a : OM — odds-ratio sub-model model_b : OM — inverse-odds-ratio sub-model fitted : combined probabilities ∈ (0, 1) residuals : ot - fitted loss_value, loglik, aic/aicc/bic/bicc coef : joint parameter vector concat(B_A, B_B) model_name : "oETS[G](MNN)(MNN)"-style string

Methods

`fit`(y[, X])
`predict`(h[, X, interval, level, side])

Attributes

`actuals`
`aic`
`aicc`
`b_value`
`bic`
`bicc`
`coef`
`distribution_`
`fitted`
`holdout_data`
`lags_used`
`loglik`
`loss_`
`loss_value`
`model`
`model_name`
`nobs`
`nparam`
`occurrence`
`residuals`
`scale`
`sigma`
`time_elapsed`