Publications

Depth-based outlier detection for grouped smart meters: a functional data analysis toolbox

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On projection methods for functional time series forecasting

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Localization Processes for Functional Data

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Visualizing outliers in high dimensional functional data for task fMRI data exploration

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Integrated depth for partially observed functional data

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Depth-based method for functional data analysis

This is my PhD thesis. Here, I proposed non-parametric methods (depth-based) to solve problems of visualization, outliers detection, forecasting and classification. In particular, it is focused on statistical units that are observed over a continumm (functional data).
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A depth for censured functional data

Censured functional data are becoming more recurrent in applications. In those cases, the existing depth measure are useless. In this …
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A depth-based method for functional time series forecasting

An approach is presented for making predictions about functional time series. The method is applied to data coming from periodically …
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Prediction bands for functional data based on depth measures.

We propose a new methodology for predicting a partially observed curve from a functional data sample. The novelty of our approach …
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