chainladder.IncrementalAdditive

class chainladder.IncrementalAdditive(trend=0.0, n_periods=-1, average='volume', future_trend=0, drop=None, drop_high=None, drop_low=None, drop_above=inf, drop_below=-inf, drop_valuation=None, preserve=1)

The Incremental Additive Method.

Parameters:

trend: float (default=0.0)

A multiplicative trend amount used to trend each incremental development period the valuation_date of the Triangle.

future_trend: float (default=None)

The trend to apply to the incremental development periods in the lower half of the completed Triangle. If None, then will be set to the value of the trend parameter.

n_periods: integer, optional (default=-1)

number of origin periods to be used in the ldf average calculation. For all origin periods, set n_periods=-1

average: str optional (default=’volume’)

type of averaging to use for average incremental factor calculation. Options include ‘regression’, ‘volume’ and ‘simple’.

drop: tuple or list of tuples

Drops specific origin/development combination(s)

drop_high: bool or list of bool (default=None)

Drops highest link ratio(s) from LDF calculation

drop_low: bool or list of bool (default=None)

Drops lowest link ratio(s) from LDF calculation

drop_above: float or list of floats (default = numpy.inf)

Drops all link ratio(s) above the given parameter from incremental factor calculation

drop_below: float or list of floats (default = numpy.NINF)

Drops all link ratio(s) below the given parameter from incremental factor calculation

preserve: int (default = 1)

The minimum number of incremental factor(s) required for incremental factor calculation

drop_valuation: str or list of str (default=None)

Drops specific valuation periods. str must be date convertible.

Attributes:

ldf_: Triangle

The estimated loss development patterns

cdf_: Triangle

The estimated cumulative development patterns

tri_zeta: Triangle

The raw incrementals as a percent of exposure trended to the valuation date of the Triangle.

fit_zeta: Triangle

The raw incrementals as a percent of exposure trended to the valuation date of the Triangle. Only those used in the fitting.

zeta_: Triangle

The fitted incrementals as a percent of exposure trended to the valuation date of the Triangle.

cum_zeta_: Triangle

The fitted cumulative percent of exposure trended to the valuation date of the Triangle

w_: ndarray

The weight used in the zeta fitting

w_tri_: Triangle

Triangle of w_

sample_weight: Triangle

The exposure used to obtain incremental factor

incremental_: Triangle

A triangle of full incremental values.

Methods

fit(X[, y, sample_weight])	Fit the model with X.
fit_transform(X[, y])	Fit to data, then transform it.
get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters for this estimator.
set_backend(backend[, inplace, deep])	Converts triangle array_backend.
set_fit_request(*[, sample_weight])	Request metadata passed to the fit method.
set_output(*[, transform])	Set output container.
set_params(**params)	Set the parameters of this estimator.
to_json()	Serializes triangle object to json format
to_pickle(path[, protocol])	Serializes triangle object to pickle.
transform(X)	If X and self are of different shapes, align self to X, else return self.

pipe