Conformal clustering for functional variables, With application to electricity consumption curves. / Nouretdinov, Ilia; Fontana, Matteo ; Gammerman, James; Shemilt, Laura ; Rehal, Daljit.

1 p. COPA 2019 : 8th Symposium on Conformal and Probabilistic Prediction with Applications. 2019, poster.

Research output: Other contribution

Published

Standard

Conformal clustering for functional variables, With application to electricity consumption curves. / Nouretdinov, Ilia; Fontana, Matteo ; Gammerman, James; Shemilt, Laura ; Rehal, Daljit.

1 p. COPA 2019 : 8th Symposium on Conformal and Probabilistic Prediction with Applications. 2019, poster.

Research output: Other contribution

Harvard

Nouretdinov, I, Fontana, M, Gammerman, J, Shemilt, L & Rehal, D 2019, Conformal clustering for functional variables, With application to electricity consumption curves. COPA 2019 : 8th Symposium on Conformal and Probabilistic Prediction with Applications.

APA

Nouretdinov, I., Fontana, M., Gammerman, J., Shemilt, L., & Rehal, D. (2019, Sep 10). Conformal clustering for functional variables, With application to electricity consumption curves. COPA 2019 : 8th Symposium on Conformal and Probabilistic Prediction with Applications.

Vancouver

Author

Nouretdinov, Ilia ; Fontana, Matteo ; Gammerman, James ; Shemilt, Laura ; Rehal, Daljit. / Conformal clustering for functional variables, With application to electricity consumption curves. 2019. COPA 2019 : 8th Symposium on Conformal and Probabilistic Prediction with Applications. 1 p.

BibTeX

@misc{20274f92db2349b0a7d2fcd60918bb64,
title = "Conformal clustering for functional variables, With application to electricity consumption curves",
abstract = "Conformal Clustering (CC) is a clustering technique which also allows for anomaly detection. It involves finding a so-called {\textquoteleft}region of conformity{\textquoteright} in the feature space: at a pre-selected significance level ε, the data points within this region are grouped into clusters, while all data points outside it are considered anomalies.In the existing literature on CC, data has typically been considered as finite-dimensional feature vectors. However, much existing data is found in functional form - for example time series data. In this work we generalise the CC technique to the domain of functional data. More specifically, we use CC to clean data from energy consumption curves, with the aim of subsequently disaggregating these energy curves into their components.Our experiments play two roles. Firstly, to validate our expectation that conformal clustering is an effective technique for data cleaning. And secondly, to confirm that a functional approach to data analysis can provide new insights that are lacking in avector approach.",
author = "Ilia Nouretdinov and Matteo Fontana and James Gammerman and Laura Shemilt and Daljit Rehal",
year = "2019",
month = sep,
day = "10",
language = "English",
publisher = "COPA 2019 : 8th Symposium on Conformal and Probabilistic Prediction with Applications",
type = "Other",

}

RIS

TY - GEN

T1 - Conformal clustering for functional variables, With application to electricity consumption curves

AU - Nouretdinov, Ilia

AU - Fontana, Matteo

AU - Gammerman, James

AU - Shemilt, Laura

AU - Rehal, Daljit

PY - 2019/9/10

Y1 - 2019/9/10

N2 - Conformal Clustering (CC) is a clustering technique which also allows for anomaly detection. It involves finding a so-called ‘region of conformity’ in the feature space: at a pre-selected significance level ε, the data points within this region are grouped into clusters, while all data points outside it are considered anomalies.In the existing literature on CC, data has typically been considered as finite-dimensional feature vectors. However, much existing data is found in functional form - for example time series data. In this work we generalise the CC technique to the domain of functional data. More specifically, we use CC to clean data from energy consumption curves, with the aim of subsequently disaggregating these energy curves into their components.Our experiments play two roles. Firstly, to validate our expectation that conformal clustering is an effective technique for data cleaning. And secondly, to confirm that a functional approach to data analysis can provide new insights that are lacking in avector approach.

AB - Conformal Clustering (CC) is a clustering technique which also allows for anomaly detection. It involves finding a so-called ‘region of conformity’ in the feature space: at a pre-selected significance level ε, the data points within this region are grouped into clusters, while all data points outside it are considered anomalies.In the existing literature on CC, data has typically been considered as finite-dimensional feature vectors. However, much existing data is found in functional form - for example time series data. In this work we generalise the CC technique to the domain of functional data. More specifically, we use CC to clean data from energy consumption curves, with the aim of subsequently disaggregating these energy curves into their components.Our experiments play two roles. Firstly, to validate our expectation that conformal clustering is an effective technique for data cleaning. And secondly, to confirm that a functional approach to data analysis can provide new insights that are lacking in avector approach.

M3 - Other contribution

PB - COPA 2019 : 8th Symposium on Conformal and Probabilistic Prediction with Applications

ER -