Data Driven Decision Making with Time Series and Spatio-temporal Data
Bin Yang, Yuxuan Liang, Chenjuan Guo, Christian S. Jensen
TL;DR
The paper addresses the challenge of extracting value from increasingly large and heterogeneous time series and spatio-temporal data to support data-driven decisions in domains like smart cities and cloud management. It proposes a data-centric tutorial framework built around the Data-Governance-Analytics-Decision paradigm, detailing data foundations, governance, analytics under the AGREE principles, and decision-making strategies, plus directions for future work. Key contributions include a cohesive synthesis of data foundations, governance methods, and decision-making strategies tailored to time series and spatio-temporal data, along with a roadmap for benchmarking and future research. The work provides researchers and practitioners with an end-to-end reference to enable greener, more efficient, and more reliable decision-making through advanced data analytics on time series and spatio-temporal data.
Abstract
Time series data captures properties that change over time. Such data occurs widely, ranging from the scientific and medical domains to the industrial and environmental domains. When the properties in time series exhibit spatial variations, we often call the data spatio-temporal. As part of the continued digitalization of processes throughout society, increasingly large volumes of time series and spatio-temporal data are available. In this tutorial, we focus on data-driven decision making with such data, e.g., enabling greener and more efficient transportation based on traffic time series forecasting. The tutorial adopts the holistic paradigm of ``data-governance-analytics-decision.'' We first introduce the data foundation of time series and spatio-temporal data, which is often heterogeneous. Next, we discuss data governance methods that aim to improve data quality. We then cover data analytics, focusing on the ``AGREE'' principles: Automation, Generalization, Robustness, Explainability, and Efficiency. We finally cover data-driven decision making strategies and briefly discuss promising research directions. We hope that the tutorial will serve as a primary resource for researchers and practitioners who are interested in value creation from time series and spatio-temporal data.