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RESEARCH ARTICLE   Open Access    

Relational time series forecasting

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  • Abstract: Networks encode dependencies between entities (people, computers, proteins) and allow us to study phenomena across social, technological, and biological domains. These networks naturally evolve over time by the addition, deletion, and changing of links, nodes, and attributes. Despite the importance of modeling these dynamics, existing work in relational machine learning has ignored relational time series data. Relational time series learning lies at the intersection of traditional time series analysis and statistical relational learning, and bridges the gap between these two fundamentally important problems. This paper formulates the relational time series learning problem, and a general framework and taxonomy for representation discovery tasks of both nodes and links including predicting their existence, label, and weight (importance), as well as systematically constructing features. We also reinterpret the prediction task leading to the proposal of two important relational time series forecasting tasks consisting of (i) relational time series classification (predicts a future class or label of an entity), and (ii) relational time series regression (predicts a future real-valued attribute or weight). Relational time series models are designed to leverage both relational and temporal dependencies to minimize forecasting error for both relational time series classification and regression. Finally, we discuss challenges and open problems that remain to be addressed.
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  • Cite this article

    Ryan A. Rossi. 2018. Relational time series forecasting. The Knowledge Engineering Review 33(1), doi: 10.1017/S0269888918000024
    Ryan A. Rossi. 2018. Relational time series forecasting. The Knowledge Engineering Review 33(1), doi: 10.1017/S0269888918000024

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RESEARCH ARTICLE   Open Access    

Relational time series forecasting

Abstract: Abstract: Networks encode dependencies between entities (people, computers, proteins) and allow us to study phenomena across social, technological, and biological domains. These networks naturally evolve over time by the addition, deletion, and changing of links, nodes, and attributes. Despite the importance of modeling these dynamics, existing work in relational machine learning has ignored relational time series data. Relational time series learning lies at the intersection of traditional time series analysis and statistical relational learning, and bridges the gap between these two fundamentally important problems. This paper formulates the relational time series learning problem, and a general framework and taxonomy for representation discovery tasks of both nodes and links including predicting their existence, label, and weight (importance), as well as systematically constructing features. We also reinterpret the prediction task leading to the proposal of two important relational time series forecasting tasks consisting of (i) relational time series classification (predicts a future class or label of an entity), and (ii) relational time series regression (predicts a future real-valued attribute or weight). Relational time series models are designed to leverage both relational and temporal dependencies to minimize forecasting error for both relational time series classification and regression. Finally, we discuss challenges and open problems that remain to be addressed.

    • The authors thank all the reviewers for many helpful suggestions and feedback.

    • This area of ML is sometimes referred to as SRL or relational machine learning.

    • A time series of relational attributed graph data.

    • © Cambridge University Press, 2018 2018Cambridge University Press
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    Ryan A. Rossi. 2018. Relational time series forecasting. The Knowledge Engineering Review 33(1), doi: 10.1017/S0269888918000024
    Ryan A. Rossi. 2018. Relational time series forecasting. The Knowledge Engineering Review 33(1), doi: 10.1017/S0269888918000024
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