Out-of-Distribution Generalization in Time Series

Tutorial in the 38th Annual AAAI Conference on Artificial Intelligence (AAAI 2024)

  • Tuesday, 20 February 2024
  • 2:00 pm - 3:45 pm (PST)
  • Vancouver, Canada

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This tutorial aims to bring together AI researchers, data scientists, and industry practitioners to explore out-of-distribution generalization challenges in time series. Time series data are prevalent in many domains, including retail, finance, healthcare, and environmental monitoring, serving as a basis for numerous applications. A major challenge in time series studies is the presence of data shifts, where the statistical properties of data may vary due to data collection from various sources, different locations, or conditions. Out-of-distribution generalization is the task where models should generalize to new, unseen scenarios/domains by learning from observed, seen scenarios. In this tutorial, we embark on a journey to explore the confluence of generalization and time series analysis, revealing insights into how out-of-distribution generalization techniques can be harnessed to tackle challenges in modeling time series data. We begin by laying a foundation of time series problems and out-of-distribution generalization. Then, we delve into the challenges, problems, methods, and evaluation in improving out-of-distribution generalization in the time series domain. Recent research findings in this emerging field and their implications will be unveiled, providing participants with the tools to advance their work. Finally, we turn toward the future, discussing emerging trends, open research questions, and the rich set of possibilities in this field.



  • Real-world scenarios and motivation
  • Background
    • Preliminaries of time series
    • Preliminaries of out-of-distribution generalization
  • Problems and challenges
  • Methodology
  • Datasets, benchmarks and evaluations
  • Summary, future directions and discussion



Slides can be downloaded from here