TimeSeek pretrains one model for multi-task time series analytics
Original title: TimeSeek: Multi-Task and Multi-Domain Pretraining for Generalized Time Series Analytics
After large-scale pretraining, authors report state-of-the-art forecasting, anomaly detection, and classification in few-shot and zero-shot settings.
TimeSeek is a proposed time series foundation model meant to handle multi-domain data and several analytics tasks in one pretraining setup. A Multi-Domain Feature Extractor uses progressive multi-scale modeling and adaptive slot channel modeling. Multi-task training combines task-specific customization with cross-task sharing. The paper says the pretrained model leads forecasting, anomaly detection, and classification under few-shot and zero-shot conditions, with favorable inference efficiency.
Unconfirmed: State-of-the-art claims are from the paper and no parameter count or dataset size is stated.
- The design targets domain and task heterogeneity through selective information sharing.
- Existing TSFMs are criticized for single-task pretraining or weak multi-task sharing.
- Reported tasks are forecasting, anomaly detection, and classification.
- Authors also claim favorable inference efficiency after pretraining.
From the original article
1 min read at arxiv.orgTime series analytics has been widely applied across various domains. While existing time series foundation models (TSFMs) have demonstrated promising results, they still face two major challenges: 1) Most TSFMs mainly rely on stacking Transformer layers to increase parameter size, lacking the ability to adaptively model complex time series patterns from multi-domain data.
By Peng Chen · arxiv.org
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