We are seeing a significant growth in the Internet of Things (IoT) and mobile applications which are based on predictive analytics over time-series data collected from various types of sensors and wearable devices. Some important applications include smart home automation, mobile health, smart grid management, and finance. Traditional machine learning and deep learning has shown great success in learning accurate predictive models from time-series data. However, safe and reliable deployment of such machine learning (ML) systems require the ability to be robust to adversarial/natural perturbations to time-series, and to detect time-series data which does not follow the distribution of training data, aka out-of-distribution (OOD) detection.

This tutorial will cover recent advances in adversarial robustness and certification for time-series domain using appropriate distance measures (e.g., dynamic time warping); min-max optimization algorithms to train robust ML models for time-series domain; OOD detection methods using deep generative models with application to generation of synthetic time-series data; threats of adversarial attack on multivariate time-series forecasting models and viable defense mechanisms. This tutorial will also cover the real-world applications that require reliable and robust time-series analytics such as classification in human activity monitoring for smart health, and regression/forecasting in financial data.

The tutorial is on Wednesday, 21st February 2024, 8:30 a.m. EST — 10:15 a.m. EST.