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 classi cation.
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 allows to study the e ect of the time series components individually and hence to isolate the relevant components and explore short term variations of the disease. Bailly et al introduce a series classi cation 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 multiple 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 e cient temporal metric by combining 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 e cient algorithm (Bubble) to sparse regular time series. By coupling both mechanisms, the nearest-neighbor classi cation 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. Speci cally, 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 symbolization procedure combined with the use of di erent 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 spatiotemporal coordinates in the future from a sample of pre- xed instances observed at di erent positions in the feature space and at di erent times in the past. Two di erent approaches using spatio-temporal kernel density estimation are proposed. 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 Structural Equation Models (SEMs) by maximizing model scores in terms of data t and complexity, showing robustness for nite samples. Salperwyck et al introduce 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 experimental 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 classi er and speci cally 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 Arti cial Intelligence (LNAI).
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. Also our gratitude to several colleagues that helped us with the organization of the workshop, particularly Saeid Soheily (Universite Grenoble Alpes, France).
The organizers of the AALTD conference gratefully thank the nancial support 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, Universite Joseph Fourier and Universidade da Corun~a.
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 signi cantly contributed to the success of AALTD 2105.
We sincerely hope that the workshop participants have a great and fruitful time at the conference.