Latent Ordinary Differential Equations for Irregularly-Sampled Time Series

by · Jun 14, 2019 · 271 views ·

ICML 2019

Time series data is both quickly growing and already ubiquitous. In domains spanning as broad a range as climate, robotics, entertainment, finance, healthcare, and transportation, there has been a significant shift away from parsimonious, infrequent measurements to nearly continuous monitoring and recording. Rapid advances in sensing technologies, ranging from remote sensors to wearables and social sensing, are generating rapid growth in the size and complexity of time series data streams. Thus, the importance and impact of time series analysis and modelling techniques only continues to grow. At the same time, while time series analysis has been extensively studied by econometricians and statisticians, modern time series data often pose significant challenges for the existing techniques both in terms of their structure (e.g., irregular sampling in hospital records and spatiotemporal structure in climate data) and size. Moreover, the focus on time series in the machine learning community has been comparatively much smaller. In fact, the predominant methods in machine learning often assume i.i.d. data streams, which is generally not appropriate for time series data. Thus, there is both a great need and an exciting opportunity for the machine learning community to develop theory, models and algorithms specifically for the purpose of processing and analyzing time series data. We see ICML as a great opportunity to bring together theoretical and applied researchers from around the world and with different backgrounds who are interested in the development and usage of time series analysis and algorithms. This includes methods for time series prediction, classification, clustering, anomaly and change point detection, causal discovery, and dimensionality reduction as well as general theory for learning and analyzing stochastic processes. Since time series have been studied in a variety of different fields and have many broad applications, we plan to host leading academic researchers and industry experts with a range of perspectives and interests as invited speakers. Moreover, we also invite researchers from the related areas of batch and online learning, deep learning, reinforcement learning, data analysis and statistics, and many others to both contribute and participate in this workshop.