ArimaModel train

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train(self, timeseries_values, p, d, q, include_intercept=True, method='css-cgd', user_init_params=None)

[ALPHA] Creates Autoregressive Integrated Moving Average (ARIMA) Model from the specified time series values.

Parameters:

timeseries_values : list

List of time series values.

p : int32

Autoregressive order

d : int32

Differencing order

q : int32

Moving average order

include_intercept : bool (default=True)

If true, the model is fit with an intercept. Default is True

method : unicode (default=css-cgd)

Objective function and optimization method. Current options are: ‘css-bobyqa’ and ‘css-cgd’. Both optimize the log likelihood in terms of the conditional sum of squares. The first uses BOBYQA for optimization, while the second uses conjugate gradient descent. Default is ‘css-cgd’.

user_init_params : list (default=None)

A set of user provided initial parameters for optimization. If the list is empty (default), initialized using Hannan-Rissanen algorithm. If provided, order of parameter should be: intercept term, AR parameters (in increasing order of lag), MA parameters (in increasing order of lag)

Returns:

: dict

Array of coefficients (intercept, AR, MA, with increasing degrees).

Given a time series, fits an non-seasonal Autoregressive Integrated Moving Average (ARIMA) model of order (p, d, q) where p represents the autoregression terms, d represents the order of differencing, and q represents the moving average error terms. If includeIntercept is true, the model is fitted with an intercept.

Examples

See :doc: ‘here <new>’ for examples.