Examples

Examples of several functionalities of the package.

Boxplot

Boxplot

Classification methods

Classification methods

Clustering

Clustering

Function composition

Function composition

Depth based classification

Depth based classification

Discretized function representation

Discretized function representation

Elastic registration

Elastic registration

Exploring data

Exploring data

Extrapolation

Extrapolation

Functional Principal Component Analysis

Functional Principal Component Analysis

Outlier detection with FPCA

Outlier detection with FPCA

Interpolation

Interpolation

K-nearest neighbors classification

K-nearest neighbors classification

Kernel Regression

Kernel Regression

Kernel Smoothing

Kernel Smoothing

Landmark registration

Landmark registration

Landmark shift

Landmark shift

Magnitude-Shape Plot

Magnitude-Shape Plot

Magnitude-Shape Plot synthetic example

Magnitude-Shape Plot synthetic example

Neighbors Functional Regression

Neighbors Functional Regression

Neighbors Scalar Regression

Neighbors Scalar Regression

One-way functional ANOVA with real data

One-way functional ANOVA with real data

One-way functional ANOVA with synthetic data

One-way functional ANOVA with synthetic data

Pairwise alignment

Pairwise alignment

Radius neighbors classification

Radius neighbors classification

Representation of functional data

Representation of functional data

Shift Registration

Shift Registration

Surface Boxplot

Surface Boxplot

ICTAI examples

Examples of complete analyses showcasing the main functionalities of the scikit-fda package, presented at the 34th IEEE International Conference on Tools with Artificial Intelligence (ICTAI).

Meteorological data: data visualization, clustering, and functional PCA

Meteorological data: data visualization, clustering, and functional PCA

Voice signals: smoothing, registration, and classification

Voice signals: smoothing, registration, and classification

Spectrometric data: derivatives, regression, and variable selection

Spectrometric data: derivatives, regression, and variable selection