Data Driven Decision Making with Time Series and Spatio-temporal Data

• URL: http://arxiv.org/abs/2503.08473v3
• Date: Wed, 02 Apr 2025 03:02:00 GMT
• Title: Data Driven Decision Making with Time Series and Spatio-temporal Data
• Authors: Bin Yang, Yuxuan Liang, Chenjuan Guo, Christian S. Jensen,
• Abstract summary: Time series data captures properties that change over time.<n>The tutorial adopts the holistic paradigm of data-analytics-decision''<n>We discuss data governance methods that aim to improve data quality.
• Score: 32.3883180161677
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Learning Latent Spaces for Domain Generalization in Time Series Forecasting [60.29403194508811]
  Time series forecasting is vital in many real-world applications, yet developing models that generalize well on unseen relevant domains remains underexplored. We propose a framework for domain generalization in time series forecasting by mining the latent factors that govern temporal dependencies across domains. Our approach uses a decomposition-based architecture with a new Conditional $beta$-Variational Autoencoder (VAE), wherein time series data is first decomposed into trend-cyclical and seasonal components.
  arXiv  Detail & Related papers  (2024-12-15T12:41:53Z)
• Tackling Data Heterogeneity in Federated Time Series Forecasting [61.021413959988216]
  Time series forecasting plays a critical role in various real-world applications, including energy consumption prediction, disease transmission monitoring, and weather forecasting. Most existing methods rely on a centralized training paradigm, where large amounts of data are collected from distributed devices to a central cloud server. We propose a novel framework, Fed-TREND, to address data heterogeneity by generating informative synthetic data as auxiliary knowledge carriers.
  arXiv  Detail & Related papers  (2024-11-24T04:56:45Z)
• ST-DPGAN: A Privacy-preserving Framework for Spatiotemporal Data Generation [19.18074489351738]
  We propose a Graph-based model for generating privacy-protected data. Experiments conducted on three real-worldtemporal datasets validate the efficacy of our model. The prediction model trained on our generated data maintains a competitive edge compared to the model trained on the original data.
  arXiv  Detail & Related papers  (2024-06-04T04:43:54Z)
• A Survey on Diffusion Models for Time Series and Spatio-Temporal Data [92.1255811066468]
  We review the use of diffusion models in time series and S-temporal data, categorizing them by model, task type, data modality, and practical application domain. We categorize diffusion models into unconditioned and conditioned types discuss time series and S-temporal data separately. Our survey covers their application extensively in various fields including healthcare, recommendation, climate, energy, audio, and transportation.
  arXiv  Detail & Related papers  (2024-04-29T17:19:40Z)
• Review of Data-centric Time Series Analysis from Sample, Feature, and Period [37.33135447969283]
  A good time-series dataset is advantageous for the model's accuracy, robustness, and convergence. The emergence of data-centric AI represents a shift in the landscape from model refinement to prioritizing data quality. We systematically review different data-centric methods in time series analysis, covering a wide range of research topics.
  arXiv  Detail & Related papers  (2024-04-24T00:34:44Z)
• Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook [95.32949323258251]
  Temporal data, notably time series andtemporal-temporal data, are prevalent in real-world applications. Recent advances in large language and other foundational models have spurred increased use in time series andtemporal data mining.
  arXiv  Detail & Related papers  (2023-10-16T09:06:00Z)
• Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domain [54.67888148566323]
  We introduce three large-scale time series forecasting datasets from the cloud operations domain. We show it is a strong zero-shot baseline and benefits from further scaling, both in model and dataset size. Accompanying these datasets and results is a suite of comprehensive benchmark results comparing classical and deep learning baselines to our pre-trained method.
  arXiv  Detail & Related papers  (2023-10-08T08:09:51Z)
• Time Series Analysis via Network Science: Concepts and Algorithms [62.997667081978825]
  This review provides a comprehensive overview of existing mapping methods for transforming time series into networks. We describe the main conceptual approaches, provide authoritative references and give insight into their advantages and limitations in a unified notation and language. Although still very recent, this research area has much potential and with this survey we intend to pave the way for future research on the topic.
  arXiv  Detail & Related papers  (2021-10-11T13:33:18Z)
• Deep learning for time series classification [2.0305676256390934]
  Time series analysis allows us to visualize and understand the evolution of a process over time. Time series classification consists of constructing algorithms dedicated to automatically label time series data. Deep learning has emerged as one of the most effective methods for tackling the supervised classification task.
  arXiv  Detail & Related papers  (2020-10-01T17:38:40Z)
• Spatiotemporal data analysis with chronological networks [4.7842701621852655]
  We propose a network-based model fortemporal data analysis called It chronnet. The main goal of this model is to represent consecutive recurrent events between cells with strong links in the network. In this paper, we describe how to use our model considering artificial and real data sets.
  arXiv  Detail & Related papers  (2020-04-23T22:50:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.