Object-Oriented Software for Functional Data

Happ-Kurz, Clara

doi:10.18637/jss.v093.i05

Cited by 20 publications

(10 citation statements)

References 23 publications

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“…The eigenfunctions ψ m (t) were constructed by splitting orthonormal Fourier functions into d = 3 pieces and shifting them to the required domain (Happ-Kurz, 2020). We set M = 9 basis functions.…”

Section: Simulation Modelsmentioning

confidence: 99%

Detecting and classifying outliers in big functional data

Ojo

Anta

Lillo

et al. 2021

Adv Data Anal Classif

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Section: Simulation Modelsmentioning

confidence: 99%

Detecting and classifying outliers in big functional data

Ojo

Anta

Lillo

et al. 2021

Adv Data Anal Classif

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“…where σ i iid ∼ U [0.1, 0.3], for i = 1, 2, 3. The eigenfunctions ψ m (t) were constructed by splitting orthonormal Fourier functions into d = 3 pieces and shifting them to the required domain (Happ-Kurz, 2020). We set M = 9 basis functions.…”

Section: Simulation Modelsmentioning

confidence: 99%

Multivariate Functional Outlier Detection using the FastMUOD Indices

Ojo¹,

Anta²,

Genton³

et al. 2022

Preprint

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We present definitions and properties of the fast massive unsupervised outlier detection (FastMUOD) indices, used for outlier detection (OD) in functional data. FastMUOD detects outliers by computing, for each curve, an amplitude, magnitude and shape index meant to target the corresponding types of outliers. Some methods adapting FastMUOD to outlier detection in multivariate functional data are then proposed. These include applying Fast-MUOD on the components of the multivariate data and using random projections. Moreover, these techniques are tested on various simulated and real multivariate functional datasets. Compared with the state of the art in multivariate functional OD, the use of random projections showed the most effective results with similar, and in some cases improved, OD performance.

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“…Fourier basis. More details can be found in Happ-Kurz (2020). Additionally, we add noise to each observation i, in the form of i which arises from the standard uniform distribution The functional observations are made using 35 Fourier basis functions and we use only the functional data associated with the raw data for the simulation scenarios.…”

Section: Simulation Studiesmentioning

confidence: 99%

Neural Networks as Functional Classifiers

Thind,

Multani,

Cao

2020

Preprint

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In recent years, there has been considerable innovation in the world of predictive methodologies. This is evident by the relative domination of machine learning approaches in various classification competitions. While these algorithms have excelled at multivariate problems, they have remained dormant in the realm of functional data analysis. We develop notable deep learning methodologies to the domain of functional data for the purpose of classification problems. We highlight the effectiveness of our method in a number of classification applications such as classification of spectrographic data. Moreover, we demonstrate the performance of our classifier through simulation studies in which we compare our approach to the functional linear model and other conventional classification methods.

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Object-Oriented Software for Functional Data

Cited by 20 publications

References 23 publications

Detecting and classifying outliers in big functional data

Detecting and classifying outliers in big functional data

Multivariate Functional Outlier Detection using the FastMUOD Indices

Neural Networks as Functional Classifiers

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