CloudCons benchmark shows foundation models' superior zero-shot forecasting does not automatically yield better resource consolidation decisions, with predictive quantile choice acting as a key lever for efficiency-reliability trade-offs.
Pushing the limits of pre-training for time series forecasting in the cloudops domain
5 Pith papers cite this work, alongside 1 external citations. Polarity classification is still indexing.
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ReGeN decomposes references into periodic, stochastic, and causal components to generate synthetic multivariate time series that preserve domain structure and support improved forecasting in low-data settings.
Zeus proposes a multi-scale Transformer with point-wise tokenization and Multi-Objective Temporal Masking to enable tuning-free performance on forecasting, interpolation, and other time series tasks.
FSA learns a mapping from feature space to autoregressive strategy space to improve zero-shot univariate time series forecasting over Transformer baselines under matched pretraining conditions.
This is the first comprehensive survey of OOD generalization methodologies for time series, organized across data distribution, representation learning, and OOD evaluation.
citing papers explorer
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CloudCons: A Comprehensive End-to-End Benchmark for Cloud Resource Consolidation
CloudCons benchmark shows foundation models' superior zero-shot forecasting does not automatically yield better resource consolidation decisions, with predictive quantile choice acting as a key lever for efficiency-reliability trade-offs.
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REGEN: Reference-Guided Synthetic Multivariate Time Series Generation for Forecasting
ReGeN decomposes references into periodic, stochastic, and causal components to generate synthetic multivariate time series that preserve domain structure and support improved forecasting in low-data settings.
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Zeus: Towards Tuning-Free Foundation Model for Time Series Analysis
Zeus proposes a multi-scale Transformer with point-wise tokenization and Multi-Objective Temporal Masking to enable tuning-free performance on forecasting, interpolation, and other time series tasks.
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Feature to Dynamics: Feature-space to Autoregression strategy for Zero-shot Time Series Forecasting
FSA learns a mapping from feature space to autoregressive strategy space to improve zero-shot univariate time series forecasting over Transformer baselines under matched pretraining conditions.
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Out-of-Distribution Generalization in Time Series: A Survey
This is the first comprehensive survey of OOD generalization methodologies for time series, organized across data distribution, representation learning, and OOD evaluation.