Proceedings of the First International Workshop on ADVANCED ANALYTICS and LEARNING on TEMPORAL DATA AALTD 2015
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Proceedings of the First International Workshop on ADVANCED ANALYTICS AND LEARNING ON TEMPORAL DATA AALTD 2015 Workshop co-located with The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2015) September 11, 2015. Porto, Portugal Edited by Ahlame Douzal-Chouakria Jose´ A. Vilar Pierre-Franc¸ois Marteau Ann E. Maharaj Andres´ M. Alonso Edoardo Otranto Irina Nicolae CEUR Workshop Proceedings Volume 1425, 2015 Proceedings of AALTD 2015 First International Workshop on \Advanced Analytics and Learning on Temporal Data" Porto, Portugal September 11, 2015 Volume Editors Ahlame Douzal-Chouakria LIG-AMA, Universit´eJoseph Fourier B^atiment Centre Equation 4, All´ede la Palestine `aGi`res UFR IM2AG, BP 53, F-38041 Grenoble Cedex 9, France E:mail: [email protected] Jos´eA. Vilar Fern´andez MODES, Departamento de Matem´aticas,Universidade da Coru~na Facultade de Inform´atica,Campus de Elvi~na,s/n, 15071 A Coru~na,Spain E:mail: [email protected] Pierre-Fran¸coisMarteau IRISA, ENSIBS, Universit´ede Bretagne Sud Campus de Tohannic, BP 573, 56017 Vannes cedex, France E:mail: [email protected] Ann E. Maharaj Department of Econometrics and Business Statistics, Monash University Caulfield Campus Building H, Level 5, Room 86 900 Dandenong Road, Caulfield East, Victoria 3145, Australia E:mail: [email protected] Andr´esM. Alonso Fern´andez Departamento de Estad´ıstica,Universidad Carlos III de Madrid C/ Madrid, 126, 28903 Getafe (Madrid) Spain E:mail: [email protected] Edoardo Otranto Department of Cognitive Sciences, Educational and Cultural Studies University of Messina Via Concezione, n.6, 98121 Messina, Italy E:mail: [email protected] Maria-Irina Nicolae Jean Monnet University, Hubert Curien Lab E105, 18 rue du Professeur Beno^ıtLauras, Saint-Etienne, France E:mail: [email protected] Copyright ⃝c 2015 Douzal, Vilar, Marteau, Maharaj, Alonso, Otranto, Nicolae Published by the editors on CEUR-WS.ORG ISSN 1613-0073 Volume 1425 http://CEUR-WS.org/Vol-1425 This volume is published and copyrighted by its editors. The copyright for individual papers is held by the papers authors. Copying is permitted for private and academic purposes. September, 2015 Preface We are honored to welcome you to the 1st International Workshop on Advanced Analytics and Learning on Temporal Data (AALTD), which is held in Porto, Portugal, on September 11th, 2015, co-located with The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2015). The aim of this workshop is to bring together researchers and experts in machine learning, data mining, pattern analysis and statistics to share their challenging issues and advance researches on temporal data analysis. Analysis and learning from temporal data cover a wide scope of tasks including learning metrics, learning representations, unsupervised feature extraction, clustering and classification. This volume contains the conference program, an abstract of the invited keynote and the set of regular papers accepted to be presented at the confer- ence. Each of the submitted papers was reviewed by at least two independent reviewers, leading to the selection of seventeen papers accepted for presentation and inclusion into the program and these proceedings. The contributions are given by the alphabetical order, by surname. An index of authors can be also found at the end of this book. The keynote given by Gustavo Camps-Valls on \Capturing Time-Structures in Earth Observation Data with Gaussian Processes" focuses on machine learn- ing models based on Gaussian processes which help to monitor land, oceans, and atmosphere through the estimation of climate and biophysical variables. The accepted papers spanned from innovative ideas on analytic of temporal data, including promising new approaches and covering both practical and the- oretical issues. Classification of time series, estimation of graphical models for temporal data, extraction of patterns from audio streams, searching causal mod- els from longitudinal data and symbolic representation of time series are only a sample of the analyzed topics. To introduce the reader, a brief presentation of the problems addressed at each of papers is given below. A novel approach to analyze the evolution of a disease incidence is presented by Andrade-Pacheco et al. The method is based on Gaussian processes and al- lows to study the effect of the time series components individually and hence to isolate the relevant components and explore short term variations of the dis- ease. Bailly et al introduce a series classification procedure based on extracting local features using the Scale-Invariant Feature Transform (SIFT) framework and then building a global representation of the series using the Bag-of-Words (BoW) approach. Billard et al propose to highlight the main structure of multi- ple sets of multivariate time series by using principal component analysis where the standard correlation structure is replaced by lagged cross-autocorrelation. The symbolic representation of time series SAXO is formalized as a hierarchical coclustering approach by Bondu et al, evaluating also its compactness in terms of coding length. A framework to learn an efficient temporal metric by combining I several basic metrics for a robust kNN is introduced by Do et al. Dupont and Marteau introduce a sparse version of Dynamic Time Warping (DTW), called coarse-DTW, and develop an efficient algorithm (Bubble) to sparse regular time series. By coupling both mechanisms, the nearest-neighbor classification of time series can be performed much faster. Gallicchio et al study the balance assessment of elderly people with time series acquired with a Wii Balance Board. A novel technique to estimate the well-known Berg Balance Scale is proposed by using a Reservoir Computing network. Gibberd and Nelson address the estimation of graphical models when data change over time. Specifically, two extensions of the Gaussian graphical model (GGM) are introduced and empirically examined. Extraction of patterns from audio data streams is investigated by Hardy et al considering a symboliza- tion procedure combined with the use of different pattern mining methods. Jain and Spiegel propose a strategy to classify time series consisting of transforming the series into a dissimilarity representation and then applying PCA followed by an SVM. Krempl addresses the problem of forecasting the density at spatio- temporal coordinates in the future from a sample of pre-fixed instances observed at different positions in the feature space and at different times in the past. Two different approaches using spatio-temporal kernel density estimation are pro- posed. A fuzzy C-medoids algorithm to cluster time series based on comparing estimated quantile autocovariance functions is presented by Lafuente-Rego and Vilar. A new algorithm for discovering causal models from longitudinal data is developed by Rahmadi et al. The method performs structure search over Struc- tural Equation Models (SEMs) by maximizing model scores in terms of data fit and complexity, showing robustness for finite samples. Salperwyck et al intro- duce a clustering technique for time series based on maximizing an inter-inertia criterion inside parallelized decision trees. An anomaly detection approach for temporal graph data based on an iterative tensor decomposition and masking procedure is presented by Sapienza et al. Soheily-Khah et al perform an exper- imental comparison of several progressive and iterative methods for averaging time series under dynamic time warping. Finally, Sorokin extends the factored gated restricted Boltzmann machine model by adding discriminative component, thus enabling it to be used as a classifier and specifically to extract translational motion from two related images. In sum, we think that all these contributions will provide valuable feedback and motivation to advance research on analysis and learning from temporal data. It is planned that extended versions of the accepted papers will be published in a special volume of Lecture Notes of Artificial Intelligence (LNAI). We wish to thank the ECML PKDD council members for giving us the op- portunity to hold the AALTD workshop within the framework of the ECML PKDD Conference and the members of the local organizing committee for their support. Also our gratitude to several colleagues that helped us with the organi- II zation of the workshop, particularly Saeid Soheily (Universit´eGrenoble Alpes, France). The organizers of the AALTD conference gratefully thank the financial sup- port of the \Programme d'Investissements d'Avenir" of the French government through the IKATS Project as well as the support received from LIG-AMA, IRISA, MODES, Universit´eJoseph Fourier and Universidade da Coru~na. Last but not least, we wish to thank the contributing authors for the high quality works and all members of the Reviewing Committee for their invaluable assistance in the selection process. All of them have significantly contributed to the success of AALTD 2105. We sincerely hope that the workshop participants have a great and fruitful time at the conference. September, 2015 Ahlame Douzal-Chouakria Jos´eA. Vilar Pierre-Fran¸coisMarteau Ann E. Maharaj Andr´esM. Alonso Edoardo Otranto Irina Nicolae III Program Committee Ahlame Douzal-Chouakria, Universit´eGrenoble Alpes, France Jos´eAntonio Vilar Fern´andez,University of A Coru~na,Spain Pierre-Fran¸coisMarteau, IRISA, Universit´ede Bretagne-Sud,