class int = 1024, is_quantile: bool = True, *args, **kwargs)[source]


A class representing a local absolute binning approach. This binning estimates a binning for every single time series on a local level and therefore implicitly acts as a scaling mechanism.

  • num_bins – The number of discrete bins/buckets that we want values to be mapped to. (default: 1024)

  • is_quantile – Whether the binning is quantile or linear. Quantile binning allocated bins based on the cumulative distribution function, while linear binning allocates evenly spaced bins. (default: True, i.e. quantile binning)

hybrid_forward(F, data: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], observed_indicator: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], scale: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol, None], rep_params: List[Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]], **kwargs) → Tuple[Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], List[Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]]][source]

Transform the data into the desired representation.

  • F

  • data – Target data.

  • observed_indicator – Target observed indicator.

  • scale – Pre-computed scale.

  • rep_params – Additional pre-computed representation parameters.

  • **kwargs, – Additional block-specfic parameters.


Tuple consisting of the transformed data, the computed scale, and additional parameters to be passed to post_transform.

Return type

Tuple[Tensor, Tensor, List[Tensor]]

post_transform(F, samples: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], scale: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], rep_params: List[Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]]) → Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol][source]

Transform samples back to the original representation.

  • samples – Samples from a distribution.

  • scale – The scale of the samples.

  • rep_params – Additional representation-specific parameters used during post transformation.


Post-transformed samples.

Return type