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Data Denoising with Centered Moving Average
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:doc:`/SingleStepClassifiers/NonCausalStep/index`

The step applies the *Centered Moving Average* algorithm to denoise one-dimensional time series data. Note that the algorithm does not need the timestamp information to denoise the data.

.. rubric:: Input Parameters

1. One-dimensional time series data

2. Filter width

.. rubric:: Output Parameters

1. Denoised time series data

.. rubric:: Workflow

.. image:: workflow.svg


.. rubric:: Algorithm

:doc:`/Algorithms/CenteredMovingAverage/index`

.. rubric:: References

- D.\  Lorenz, T. Koehler, A comparison of denoising methods for one dimensional time series, DFG-Schwerpunktprogramm 1114, Preprint 74, 2005.

  `http://www.math.uni-bremen.de/zetem/DFG-Schwerpunkt/preprints/orig/lorenz20051dreport.pdf <http://www.math.uni-bremen.de/zetem/DFG-Schwerpunkt/preprints/orig/lorenz20051dreport.pdf>`__