Time Series Cluster Kernel for Learning Similarities between Multivariate Time Series with Missing Data

Similarity-based approaches represent a promising direction for time series analysis. However, many such methods rely on parameter tuning, and some have shortcomings if the time series are multivariate (MTS), due to dependencies between attributes, or the time series contain missing data. In this paper, we address these challenges within the powerful context of kernel methods by proposing the robust \emph{time series cluster kernel} (TCK). The approach taken leverages the missing data handling properties of Gaussian mixture models (GMM) augmented with informative prior distributions. An ensemble learning approach is exploited to ensure robustness to parameters by combining the clustering results of many GMM to form the final kernel. We evaluate the TCK on synthetic and real data and compare to other state-of-the-art techniques. The experimental results demonstrate that the TCK is robust to parameter choices, provides competitive results for MTS without missing data and outstanding results for missing data.Comment: 23 pages, 6 figure

Deep learning for time series classification: a review

Time Series Classification (TSC) is an important and challenging problem in data mining. With the increase of time series data availability, hundreds of TSC algorithms have been proposed. Among these methods, only a few have considered Deep Neural Networks (DNNs) to perform this task. This is surprising as deep learning has seen very successful applications in the last years. DNNs have indeed revolutionized the field of computer vision especially with the advent of novel deeper architectures such as Residual and Convolutional Neural Networks. Apart from images, sequential data such as text and audio can also be processed with DNNs to reach state-of-the-art performance for document classification and speech recognition. In this article, we study the current state-of-the-art performance of deep learning algorithms for TSC by presenting an empirical study of the most recent DNN architectures for TSC. We give an overview of the most successful deep learning applications in various time series domains under a unified taxonomy of DNNs for TSC. We also provide an open source deep learning framework to the TSC community where we implemented each of the compared approaches and evaluated them on a univariate TSC benchmark (the UCR/UEA archive) and 12 multivariate time series datasets. By training 8,730 deep learning models on 97 time series datasets, we propose the most exhaustive study of DNNs for TSC to date.Comment: Accepted at Data Mining and Knowledge Discover

Learning Human Behaviour Patterns by Trajectory and Activity Recognition

The world’s population is ageing, increasing the awareness of neurological and behavioural impairments that may arise from the human ageing. These impairments can be manifested by cognitive conditions or mobility reduction. These conditions are difficult to be detected on time, relying only on the periodic medical appointments. Therefore, there is a lack of routine screening which demands the development of solutions to better assist and monitor human behaviour. The available technologies to monitor human behaviour are limited to indoors and require the installation of sensors around the user’s homes presenting high maintenance and installation costs. With the widespread use of smartphones, it is possible to take advantage of their sensing information to better assist the elderly population. This study investigates the question of what we can learn about human pattern behaviour from this rich and pervasive mobile sensing data. A deployment of a data collection over a period of 6 months was designed to measure three different human routines through human trajectory analysis and activity recognition comprising indoor and outdoor environment. A framework for modelling human behaviour was developed using human motion features, extracted in an unsupervised and supervised manner. The unsupervised feature extraction is able to measure mobility properties such as step length estimation, user points of interest or even locomotion activities inferred from an user-independent trained classifier. The supervised feature extraction was design to be user-dependent as each user may have specific behaviours that are common to his/her routine. The human patterns were modelled through probability density functions and clustering approaches. Using the human learned patterns, inferences about the current human behaviour were continuously quantified by an anomaly detection algorithm, where distance measurements were used to detect significant changes in behaviour. Experimental results demonstrate the effectiveness of the proposed framework that revealed an increase potential to learn behaviour patterns and detect anomalies

Data Mining in Internet of Things Systems: A Literature Review

The Internet of Things (IoT) and cloud technologies have been the main focus of recent research, allowing for the accumulation of a vast amount of data generated from this diverse environment. These data include without any doubt priceless knowledge if could correctly discovered and correlated in an efficient manner. Data mining algorithms can be applied to the Internet of Things (IoT) to extract hidden information from the massive amounts of data that are generated by IoT and are thought to have high business value. In this paper, the most important data mining approaches covering classification, clustering, association analysis, time series analysis, and outlier analysis from the knowledge will be covered. Additionally, a survey of recent work in in this direction is included. Another significant challenges in the field are collecting, storing, and managing the large number of devices along with their associated features. In this paper, a deep look on the data mining for the IoT platforms will be given concentrating on real applications found in the literatur

Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

Latent Structured Models for Video Understanding

The proliferation of videos in recent years has spurred a surge of interest in developing efficient techniques for automatic video interpretation. The thesis improves the understanding of videos by building structured models that use latent information to detect and recognize instances of actions or abnormalities in videos. The thesis also proposes efficient algorithms for inference in and learning of the proposed latent structured models that are appropriate for learning with weak supervision. An important class of latent variable models is the multiple instance learning where the training labels are provided only for bags of instances, but not for instances themselves. As inference of latent instance labels is performed jointly with training of a classifier on the same data, multiple-instance learning is very susceptible to overfitting. To increase the robustness of popular methods for multiple instance learning, the thesis introduces a novel concept of superbags (ensemble of bags of bags) that allows for decoupling of classifier training and latent label inference steps. In the thesis, a novel latent structured representation is proposed to discover instances of action classes in videos and jointly train an action classifier on them. Action class instances typically occupy only a part of the whole video that is not annotated in weakly labeled training videos. Therefore, multiple instance learning is proposed to find these latent action instances in training videos and jointly train the action classifier. The thesis proposes a sequential method to multiple instance learning to increase the robustness of the training. For the interpretation of crowded scenes, it is important to detect all irregular objects or actions in a video. However, the abnormality detection is hindered by the fact that the training set does not contain any abnormal sample, thus it is necessary to find abnormalities in a test video without actually knowing what they are. To address this problem, the thesis proposes a probabilistic graphical model for video parsing that searches for latent object hypotheses to jointly explain all the foreground pixels, which are, at the same time, well matched to the normal training samples. By inferring all latent normal hypotheses in a video, the model indirectly finds abnormalities as those hypotheses that are not supported by normal samples but still need to be used to explain the foreground. Video parsing is applied sequentially on individual video frames, where hypotheses are jointly inferred by a local search in a graphical model. The thesis then proposes a spatio-temporal extension of the video parsing, where an efficient inference method based on convex optimization is developed to find abnormal/normal spatio-temporal hypotheses in the video

Activity recognition using a supervised non-parametric hierarchical HMM

The problem of classifying human activities occurring in depth image sequences is addressed. The 3D joint positions of a human skeleton and the local depth image pattern around these joint positions define the features. A two level hierarchical Hidden Markov Model (H-HMM), with independent Markov chains for the joint positions and depth image pattern, is used to model the features. The states corresponding to the H-HMM bottom level characterize the granular poses while the top level characterizes the coarser actions associated with the activities. Further, the H-HMM is based on a Hierarchical Dirichlet Process (HDP), and is fully non-parametric with the number of pose and action states inferred automatically from data. This is a significant advantage over classical HMM and its extensions. In order to perform classification, the relationships between the actions and the activity labels are captured using multinomial logistic regression. The proposed inference procedure ensures alignment of actions from activities with similar labels. Our construction enables information sharing, allows incorporation of unlabelled examples and provides a flexible factorized representation to include multiple data channels. Experiments with multiple real world datasets show the efficacy of our classification approach