Time series data is ubiquitous. In domains as diverse as finance, entertainment, transportation and health-care, there has been a fundamental shift away from parsimonious, infrequent measurement to nearly continuous monitoring and recording. Rapid advances in many sensing technologies, ranging from remote sensors to wearables and social sensing, are have generated a rapid growth in the size and complexity of time series archives. Thus, while time series analysis has been studied extensively in the past, its importance only continues to grow. Furthermore, modern time series data pose significant new challenges in terms of structure (e.g., irregular sampling in hospital records and spatiotemporal structure in climate data) and size (e.g. computation and storage). These challenges are compounded by the fact that the standard i.i.d. assumptions used in other areas of machine learning are often not appropriate for time series. Instead, new theory, models and algorithms are needed to process and analyze this data.

The goal of this workshop is to bring together both theoretical and applied researchers interested in the analysis of time series and the development of new algorithms to process sequential data. This includes researchers designing algorithms for specific tasks including time series prediction, classification, clustering, anomaly and change point detection, correlation discovery, dimensionality reduction as well as researchers who work on developing a general theory for learning and understanding stochastic processes. We also invite researchers from related areas, including but not limited to batch and online learning, reinforcement learning, data analysis and statistics, and econometrics, to contribute to this workshop.