Preprocessing ============= Sometimes we need to preprocess the data prior to analyze it. The modules in this category deal with this problem. .. toctree:: :titlesonly: :maxdepth: 4 :caption: Modules: :hidden: preprocessing/missing preprocessing/smoothing preprocessing/registration preprocessing/dim_reduction preprocessing/feature_construction Missing data ------------ When the observations contains missing data, it is necessary to reconstruct the invalid information before processing it further. :doc:`Here ` you can learn more about this procedure. Smoothing --------- If the functional data observations are noisy, *smoothing* the data allows a better representation of the true underlying functions. You can learn more about the smoothing methods provided by scikit-fda :doc:`here `. Registration ------------ Sometimes, the functional data may be misaligned, or the phase variation should be ignored in the analysis. To align the data and eliminate the phase variation, we need to use *registration* methods. :doc:`Here ` you can learn more about the registration methods available in the library. Dimensionality Reduction ------------------------ The functional data may have too many features so we cannot analyse the data with clarity. To better understand the data, we need to use *dimensionality reduction* methods that can reduce the number of features while still preserving the most relevant information. :doc:`Here ` you can learn more about the dimension reduction methods available in the library. Feature construction -------------------- When dealing with functional data we might want to construct new features that can be used as additional inputs to the machine learning algorithms. The expectation is that these features make explicit characteristics that facilitate the learning process. To construct new features from the curves, *feature construction* methods are available. :doc:`Here ` you can learn more about the feature construction methods available in the library.