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Jairo Cugliari
Université de Lyon, Lyon 2, ERIC UR3083, France

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Journal article
Published: 15 March 2021 in Expert Systems with Applications
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Identifying changes in the dynamics of a classification scheme is an important task to solve using textual data streams. Changes in the volume of documents classified into one category could be a sign of a new emerging structure, which therefore gives clues on the need to update the classification scheme. In this paper, we present a method based on forecasting techniques, change detection and time series monitoring in order to raise alerts as soon as a change occurs in the volume of a given category. We build features only based on the textual content that enable us to accurately predict the expected temporal evolution of such category. Then, we use statistical process control to determine if the current volume is too far away from the one we might expect. We test our method on the New York Times Annotated Corpus and on an industrial data set from Electricité de France (EDF) and we observe that it raises alerts at the right time compared to other techniques from the literature.

ACS Style

Clément Christophe; Julien Velcin; Jairo Cugliari; Philippe Suignard; Manel Boumghar. Change detection in textual classification with unexpected dynamics. Expert Systems with Applications 2021, 176, 114831 .

AMA Style

Clément Christophe, Julien Velcin, Jairo Cugliari, Philippe Suignard, Manel Boumghar. Change detection in textual classification with unexpected dynamics. Expert Systems with Applications. 2021; 176 ():114831.

Chicago/Turabian Style

Clément Christophe; Julien Velcin; Jairo Cugliari; Philippe Suignard; Manel Boumghar. 2021. "Change detection in textual classification with unexpected dynamics." Expert Systems with Applications 176, no. : 114831.

Preprint
Published: 25 October 2019
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While ubiquitous, textual sources of information such as company reports, social media posts, etc. are hardly included in prediction algorithms for time series, despite the relevant information they may contain. In this work, openly accessible daily weather reports from France and the United-Kingdom are leveraged to predict time series of national electricity consumption, average temperature and wind-speed with a single pipeline. Two methods of numerical representation of text are considered, namely traditional Term Frequency - Inverse Document Frequency (TF-IDF) as well as our own neural word embedding. Using exclusively text, we are able to predict the aforementioned time series with sufficient accuracy to be used to replace missing data. Furthermore the proposed word embeddings display geometric properties relating to the behavior of the time series and context similarity between words.

ACS Style

David Obst; Badih Ghattas; Sandra Claudel; Jairo Cugliari; Yannig Goude; Georges Oppenheim. Textual Data for Time Series Forecasting. 2019, 1 .

AMA Style

David Obst, Badih Ghattas, Sandra Claudel, Jairo Cugliari, Yannig Goude, Georges Oppenheim. Textual Data for Time Series Forecasting. . 2019; ():1.

Chicago/Turabian Style

David Obst; Badih Ghattas; Sandra Claudel; Jairo Cugliari; Yannig Goude; Georges Oppenheim. 2019. "Textual Data for Time Series Forecasting." , no. : 1.

Conference paper
Published: 01 May 2019 in Advances in Intelligent Systems and Computing
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In industrial context, event logging is a widely accepted concept supported by most applications, services, network devices, and other IT systems. Event logs usually provide important information about security incidents, system faults or performance issues. In this way, the analysis of data from event logs is essential to extract key information in order to highlight features and patterns to understand and identify reasons of failures or faults. Our objective is to help anticipate equipment failures to allow for advance scheduling of corrective maintenance. We propose a supervised approach to predict faults from an event log dataset using wavelets features as input of a random forest which is an ensemble learning method.

ACS Style

Stéphane Bonnevay; Jairo Cugliari; Victoria Granger. Predictive Maintenance from Event Logs Using Wavelet-Based Features: An Industrial Application. Advances in Intelligent Systems and Computing 2019, 132 -141.

AMA Style

Stéphane Bonnevay, Jairo Cugliari, Victoria Granger. Predictive Maintenance from Event Logs Using Wavelet-Based Features: An Industrial Application. Advances in Intelligent Systems and Computing. 2019; ():132-141.

Chicago/Turabian Style

Stéphane Bonnevay; Jairo Cugliari; Victoria Granger. 2019. "Predictive Maintenance from Event Logs Using Wavelet-Based Features: An Industrial Application." Advances in Intelligent Systems and Computing , no. : 132-141.

Original paper
Published: 15 April 2019 in Computational Statistics
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In this work we give new density estimators by averaging classical density estimators such as the histogram, the frequency polygon and the kernel density estimators obtained over different bootstrap samples of the original data. Using existent results, we prove the \(L^2\)-consistency of these new estimators and compare them to several similar approaches by simulations. Based on them, we give also a way to construct non-parametric pointwise variability band for the target density.

ACS Style

Mathias Bourel; Jairo Cugliari. Bagging of density estimators. Computational Statistics 2019, 34, 1849 -1869.

AMA Style

Mathias Bourel, Jairo Cugliari. Bagging of density estimators. Computational Statistics. 2019; 34 (4):1849-1869.

Chicago/Turabian Style

Mathias Bourel; Jairo Cugliari. 2019. "Bagging of density estimators." Computational Statistics 34, no. 4: 1849-1869.

Conference paper
Published: 28 December 2018 in Springer Texts in Business and Economics
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The development of new electricity generation technologies has given new opportunities to developing economies. These economies are often highly dependent on fossil sources and so on the price of petrol. Uruguay has finished the transformation of its energetic mix, presenting today a very large participation of renewable sources among its production mix. This rapid change has demanded new mathematical and computing methods for the administration and monitoring of the system load. In this work we present enercast, a R package that contains prediction models that can be used by the network operator. The prediction models are divided in two groups, exogenous and endogenous models, that respectively uses external covariates or not. Each model is used to produce daily prediction which are then combined using a sequential aggregation algorithm. We show by numerical experiments the appropriateness of our end-to-end procedure applied to real data from the Uruguayan electrical system.

ACS Style

Andrés Castrillejo; Jairo Cugliari; Fernando Massa; Ignacio Ramirez. Electricity Demand Forecasting: The Uruguayan Case. Springer Texts in Business and Economics 2018, 119 -136.

AMA Style

Andrés Castrillejo, Jairo Cugliari, Fernando Massa, Ignacio Ramirez. Electricity Demand Forecasting: The Uruguayan Case. Springer Texts in Business and Economics. 2018; ():119-136.

Chicago/Turabian Style

Andrés Castrillejo; Jairo Cugliari; Fernando Massa; Ignacio Ramirez. 2018. "Electricity Demand Forecasting: The Uruguayan Case." Springer Texts in Business and Economics , no. : 119-136.

Book chapter
Published: 13 November 2018 in Wiley StatsRef: Statistics Reference Online
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In this article, the focus is on short‐term demand forecasting at some aggregate level (e.g., zone or nationwide demands) from data with at least hourly sampled data. The main salient features of the load curve are first highlighted. Some of the common covariates used in the prediction task are also discussed. Then, some basic or now classical methodological approaches for electricity demand forecasting are detailed. Finally, a presentation of new and open problems is postponed to the last section.

ACS Style

Jairo Cugliari; Jean‐Michel Poggi. Electricity Demand Forecasting. Wiley StatsRef: Statistics Reference Online 2018, 1 -8.

AMA Style

Jairo Cugliari, Jean‐Michel Poggi. Electricity Demand Forecasting. Wiley StatsRef: Statistics Reference Online. 2018; ():1-8.

Chicago/Turabian Style

Jairo Cugliari; Jean‐Michel Poggi. 2018. "Electricity Demand Forecasting." Wiley StatsRef: Statistics Reference Online , no. : 1-8.

Journal article
Published: 20 July 2018 in Energies
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Smart grids require flexible data driven forecasting methods. We propose clustering tools for bottom-up short-term load forecasting. We focus on individual consumption data analysis which plays a major role for energy management and electricity load forecasting. The first section is dedicated to the industrial context and a review of individual electrical data analysis. Then, we focus on hierarchical time-series for bottom-up forecasting. The idea is to decompose the global signal and obtain disaggregated forecasts in such a way that their sum enhances the prediction. This is done in three steps: identify a rather large number of super-consumers by clustering their energy profiles, generate a hierarchy of nested partitions and choose the one that minimize a prediction criterion. Using a nonparametric model to handle forecasting, and wavelets to define various notions of similarity between load curves, this disaggregation strategy gives a 16% improvement in forecasting accuracy when applied to French individual consumers. Then, this strategy is implemented using R—the free software environment for statistical computing—so that it can scale when dealing with massive datasets. The proposed solution is to make the algorithm scalable combine data storage, parallel computing and double clustering step to define the super-consumers. The resulting software is openly available.

ACS Style

Benjamin Auder; Jairo Cugliari; Yannig Goude; Jean-Michel Poggi. Scalable Clustering of Individual Electrical Curves for Profiling and Bottom-Up Forecasting. Energies 2018, 11, 1893 .

AMA Style

Benjamin Auder, Jairo Cugliari, Yannig Goude, Jean-Michel Poggi. Scalable Clustering of Individual Electrical Curves for Profiling and Bottom-Up Forecasting. Energies. 2018; 11 (7):1893.

Chicago/Turabian Style

Benjamin Auder; Jairo Cugliari; Yannig Goude; Jean-Michel Poggi. 2018. "Scalable Clustering of Individual Electrical Curves for Profiling and Bottom-Up Forecasting." Energies 11, no. 7: 1893.

Preprint
Published: 14 May 2018
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ACS Style

Andrés Castrillejo; Jairo Cugliari; Fernando Massa; Ignacio Ramirez. Electricity Demand Forecasting: the Uruguayan Case. 2018, 1 .

AMA Style

Andrés Castrillejo, Jairo Cugliari, Fernando Massa, Ignacio Ramirez. Electricity Demand Forecasting: the Uruguayan Case. . 2018; ():1.

Chicago/Turabian Style

Andrés Castrillejo; Jairo Cugliari; Fernando Massa; Ignacio Ramirez. 2018. "Electricity Demand Forecasting: the Uruguayan Case." , no. : 1.

Journal article
Published: 02 May 2018 in Energies
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In the past several years, the liberalization of the electricity supply, the increase in variability of electric appliances and their use, and the need to respond to the electricity demand in real time has made electricity demand forecasting a challenge. To this challenge, many solutions are being proposed. The electricity demand involves many sources such as economic activities, household need and weather sources. All of these sources make electricity demand forecasting difficult. To forecast the electricity demand, some proposed parametric methods that integrate main variables that are sources of electricity demand. Others proposed a non parametric method such as pattern recognition methods. In this paper, we propose to take only the past electricity consumption information embedded in a functional vector autoregressive state space model to forecast the future electricity demand. The model we proposed aims to be applied at some aggregation level between regional and nation-wide grids. To estimate the parameters of this model, we use likelihood maximization, spline smoothing, functional principal components analysis and Kalman filtering. Through numerical experiments on real datasets, both from supplier Enercoop and from the Transmission System Operator of the French nation-wide grid, we show the appropriateness of the approach.

ACS Style

Komi Nagbe; Jairo Cugliari; Julien Jacques. Short-Term Electricity Demand Forecasting Using a Functional State Space Model. Energies 2018, 11, 1120 .

AMA Style

Komi Nagbe, Jairo Cugliari, Julien Jacques. Short-Term Electricity Demand Forecasting Using a Functional State Space Model. Energies. 2018; 11 (5):1120.

Chicago/Turabian Style

Komi Nagbe; Jairo Cugliari; Julien Jacques. 2018. "Short-Term Electricity Demand Forecasting Using a Functional State Space Model." Energies 11, no. 5: 1120.

Journal article
Published: 01 July 2016 in International Journal of Forecasting
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ACS Style

Anestis Antoniadis; Xavier Brossat; Jairo Cugliari; Jean-Michel Poggi. A prediction interval for a function-valued forecast model: Application to load forecasting. International Journal of Forecasting 2016, 32, 939 -947.

AMA Style

Anestis Antoniadis, Xavier Brossat, Jairo Cugliari, Jean-Michel Poggi. A prediction interval for a function-valued forecast model: Application to load forecasting. International Journal of Forecasting. 2016; 32 (3):939-947.

Chicago/Turabian Style

Anestis Antoniadis; Xavier Brossat; Jairo Cugliari; Jean-Michel Poggi. 2016. "A prediction interval for a function-valued forecast model: Application to load forecasting." International Journal of Forecasting 32, no. 3: 939-947.

Conference paper
Published: 01 April 2016 in 2016 IEEE International Energy Conference (ENERGYCON)
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Electricity load forecasting is crucial for utilities for production planning as well as marketing offers. Recently, the increasing deployment of smart grids infrastructure requires the development of more flexible data driven forecasting methods adapting quite automatically to new data sets. We propose to build clustering tools useful for forecasting the load consumption. The idea is to disaggregate the global signal in such a way that the sum of disaggregated forecasts significantly improves the prediction of the whole global signal. The strategy is in three steps: first we cluster curves defining super-consumers, then we build a hierarchy of partitions within which the best one is finally selected with respect to a disaggregated forecast criterion. The proposed strategy is applied to a dataset of individual consumers from the French electricity provider EDF. A substantial gain of 16 % in forecast accuracy comparing to the 1 cluster approach is provided by disaggregation while preserving meaningful classes of consumers.

ACS Style

Jairo Cugliari; Yannig Goude; Jean-Michel Poggi. Disaggregated electricity forecasting using wavelet-based clustering of individual consumers. 2016 IEEE International Energy Conference (ENERGYCON) 2016, 1 -6.

AMA Style

Jairo Cugliari, Yannig Goude, Jean-Michel Poggi. Disaggregated electricity forecasting using wavelet-based clustering of individual consumers. 2016 IEEE International Energy Conference (ENERGYCON). 2016; ():1-6.

Chicago/Turabian Style

Jairo Cugliari; Yannig Goude; Jean-Michel Poggi. 2016. "Disaggregated electricity forecasting using wavelet-based clustering of individual consumers." 2016 IEEE International Energy Conference (ENERGYCON) , no. : 1-6.

Book chapter
Published: 01 January 2015 in Dependence in Probability and Statistics
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In hierarchical time series (HTS) forecasting, the hierarchical relation between multiple time series is exploited to make better forecasts. This hierarchical relation implies one or more aggregate consistency constraints that the series are known to satisfy. Many existing approaches, like for example bottom-up or top-down forecasting, therefore attempt to achieve this goal in a way that guarantees that the forecasts will also be aggregate consistent. We propose to split the problem of HTS into two independent steps: first one comes up with the best possible forecasts for the time series without worrying about aggregate consistency; and then a reconciliation procedure is used to make the forecasts aggregate consistent. We introduce a Game-Theoretically OPtimal (GTOP) reconciliation method, which is guaranteed to only improve any given set of forecasts. This opens up new possibilities for constructing the forecasts. For example, it is not necessary to assume that bottom-level forecasts are unbiased, and aggregate forecasts may be constructed by regressing both on bottom-level forecasts and on other covariates that may only be available at the aggregate level. We illustrate the benefits of our approach both on simulated data and on real electricity consumption data.

ACS Style

Tim Van Erven; Jairo Cugliari. Game-Theoretically Optimal Reconciliation of Contemporaneous Hierarchical Time Series Forecasts. Dependence in Probability and Statistics 2015, 297 -317.

AMA Style

Tim Van Erven, Jairo Cugliari. Game-Theoretically Optimal Reconciliation of Contemporaneous Hierarchical Time Series Forecasts. Dependence in Probability and Statistics. 2015; ():297-317.

Chicago/Turabian Style

Tim Van Erven; Jairo Cugliari. 2015. "Game-Theoretically Optimal Reconciliation of Contemporaneous Hierarchical Time Series Forecasts." Dependence in Probability and Statistics , no. : 297-317.

Journal article
Published: 01 April 2014 in International Journal of Forecasting
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ACS Style

Raphael Nedellec; Jairo Cugliari; Yannig Goude. GEFCom2012: Electric load forecasting and backcasting with semi-parametric models. International Journal of Forecasting 2014, 30, 375 -381.

AMA Style

Raphael Nedellec, Jairo Cugliari, Yannig Goude. GEFCom2012: Electric load forecasting and backcasting with semi-parametric models. International Journal of Forecasting. 2014; 30 (2):375-381.

Chicago/Turabian Style

Raphael Nedellec; Jairo Cugliari; Yannig Goude. 2014. "GEFCom2012: Electric load forecasting and backcasting with semi-parametric models." International Journal of Forecasting 30, no. 2: 375-381.

Preprint
Published: 18 December 2013
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In hierarchical time series (HTS) forecasting, the hierarchical relation be- tween multiple time series is exploited to make better forecasts. This hierarchical relation implies one or more aggregate consistency constraints that the series are known to satisfy. Many existing approaches, like for example bottom-up or top- down forecasting, therefore attempt to achieve this goal in a way that guarantees that the forecasts will also be aggregate consistent. We propose to split the problem of HTS into two independent steps: first one comes up with the best possible fore- casts for the time series without worrying about aggregate consistency; and then a reconciliation procedure is used to make the forecasts aggregate consistent. We introduce a Game-Theoretically OPtimal (GTOP) reconciliation method, which is guaranteed to only improve any given set of forecasts. This opens up new possibil- ities for constructing the forecasts. For example, it is not necessary to assume that bottom-level forecasts are unbiased, and aggregate forecasts may be constructed by regressing both on bottom-level forecasts and on other covariates that may only be available at the aggregate level. We illustrate the benefits of our approach both on simulated data and on real electricity consumption data.

ACS Style

Tim Van Erven; Jairo Cugliari. Game-theoretically Optimal Reconciliation of Contemporaneous Hierarchical Time Series Forecasts. 2013, 1 .

AMA Style

Tim Van Erven, Jairo Cugliari. Game-theoretically Optimal Reconciliation of Contemporaneous Hierarchical Time Series Forecasts. . 2013; ():1.

Chicago/Turabian Style

Tim Van Erven; Jairo Cugliari. 2013. "Game-theoretically Optimal Reconciliation of Contemporaneous Hierarchical Time Series Forecasts." , no. : 1.

Journal article
Published: 01 January 2013 in International Journal of Wavelets, Multiresolution and Information Processing
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We present two strategies for detecting patterns and clusters in high-dimensional time-dependent functional data. The use on wavelet-based similarity measures, since wavelets are well suited for identifying highly discriminant local time and scale features. The multiresolution aspect of the wavelet transform provides a time-scale decomposition of the signals allowing to visualize and to cluster the functional data into homogeneous groups. For each input function, through its empirical orthogonal wavelet transform the first strategy uses the distribution of energy across scales to generate a representation that can be sufficient to make the signals well distinguishable. Our new similarity measure combined with an efficient feature selection technique in the wavelet domain is then used within more or less classical clustering algorithms to effectively differentiate among high-dimensional populations. The second strategy uses a similarity measure between the whole time-scale representations that is based on wavelet-coherence tools. The clustering is then performed using a k-centroid algorithm starting from these similarities. Practical performance is illustrated through simulations as well as daily profiles of the French electricity power demand.

ACS Style

Anestis Antoniadis; Xavier Brossat; Jairo Cugliari; Jean-Michel Poggi. CLUSTERING FUNCTIONAL DATA USING WAVELETS. International Journal of Wavelets, Multiresolution and Information Processing 2013, 11, 1 .

AMA Style

Anestis Antoniadis, Xavier Brossat, Jairo Cugliari, Jean-Michel Poggi. CLUSTERING FUNCTIONAL DATA USING WAVELETS. International Journal of Wavelets, Multiresolution and Information Processing. 2013; 11 (1):1.

Chicago/Turabian Style

Anestis Antoniadis; Xavier Brossat; Jairo Cugliari; Jean-Michel Poggi. 2013. "CLUSTERING FUNCTIONAL DATA USING WAVELETS." International Journal of Wavelets, Multiresolution and Information Processing 11, no. 1: 1.