Abstract
Sensors in cyber-physical systems often capture interconnected processes and thus emit correlated time series (CTS), the forecasting of which enables important applications. The key to successful CTS forecasting is to uncover the temporal dynamics of time series and the spatial correlations among time series. Deep learning-based solutions exhibit impressive performance at discerning these aspects. In particular, automated CTS forecasting, where the design of an optimal deep learning architecture is automated, enables forecasting accuracy that surpasses what has been achieved by manual approaches. However, automated CTS solutions remain in their infancy and are only able to find optimal architectures for predefined hyperparameters and scale poorly to large-scale CTS. To overcome these limitations, we propose AutoCTS+, a joint, scalable framework, to automatically devise effective CTS forecasting models. Specifically, we encode each candidate architecture and accompanying hyperparameters into a joint graph representation. We introduce an efficient Architecture-Hyperparameter Comparator (AHC) to rank all architecture-hyperparameter pairs, and we then further evaluate the top-ranked pairs to select an architecture-hyperparameter pair as the final model. Extensive experiments on six benchmark datasets demonstrate that AutoCTS+ not only eliminates manual efforts but also is capable of better performance than manually designed and existing automatically designed CTS models. In addition, it shows excellent scalability to large CTS.
Original language | English |
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Journal | Proceedings of the ACM on Management of Data |
Volume | 1 |
Issue number | 1 |
Pages (from-to) | 97:1-97:26 |
ISSN | 2836-6573 |
DOIs | |
Publication status | Published - 2023 |
Event | 2023 ACM/SIGMOD International Conference on Management of Data, SIGMOD 2023 - Seattle, United States Duration: 18 Jun 2023 → 23 Jun 2023 |
Conference
Conference | 2023 ACM/SIGMOD International Conference on Management of Data, SIGMOD 2023 |
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Country/Territory | United States |
City | Seattle |
Period | 18/06/2023 → 23/06/2023 |
Sponsor | ACM SIGMOD |