We are honored to welcome you to the 2nd International Workshop on Advanced Analyt-ics and Learning on Temporal Data (AALTD), which is held in Riva del Garda, Italy, on September 19th, 2016, co-located with The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2016). 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 keynotes and the set of regular papers accepted to be presented at the conference. Each of the submitted papers was reviewed by at least two independent reviewers, leading to the selection of eleven papers accepted for presentation and inclusion into the program and these proceedings. The contributions are given by the alphabetical order, by surname. The keynote given by Marco Cuturi on "Regularized DTW Divergences for Time Se-ries" focuses on the definition of alignment kernels for time series that can later be used at the core of standard machine learning algorithms. The one given by Tony Bagnall on "The Great Time Series Classification Bake Off" presents an important attempt to experimentally compare performance of a wide range of time series classifiers, together with ensemble classifiers that aim at combining existing classifiers to improve classification quality. Accepted papers spanned from innovative ideas on analytic of temporal data, including promising new approaches and covering both practical and theoretical issues. We wish to thank the ECML PKDD council members for giving us the opportunity to hold the AALTD workshop within the framework of the ECML/PKDD Conference and the members of the local organizing committee for their support. The organizers of the AALTD conference gratefully thank the financial support of the Université de Rennes 2, MODES and Universidade da Coruña. 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 iii selection process. All of them have significantly contributed to the success of AALTD 2106. We sincerely hope that the workshop participants have a great and fruitful time at the conference.
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