A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
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This paper introduces in-context fine-tuning for time-series foundation models, enabling adaptation to new datasets without updating model weights.
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Proposes Lag-Llama, a univariate probabilistic time-series forecasting model designed for scaling law analyses of time series foundation models.
Mononito Goswami, Konrad Szafer, Arjun Choudhry, et al.
This paper introduces Moment, a family of open time-series foundation models trained from scratch, along with empirical insights and evaluations for time-series tasks.
Chen Shao, Elias Giacoumidis, Syed Moktacim Billah, et al.
This survey reviews machine learning applications in short-reach optical systems, introducing a taxonomy for time-series methods and addressing complexity challenges for practical deployment.
Hongnan Ma, Yiwei Shi, Mengyue Yang, et al.
Introduces TimePNS, a necessity-aware framework for time-series explanation that uses counterfactual interventions to identify subsequences essential for model predictions.
Kukjin Choi, Jihun Yi, Changhwa Park, et al.
This paper reviews deep learning methods for anomaly detection in multivariate time-series data, analyzes state-of-the-art models on benchmarks, and provides guidelines for model selection and training.