2 个仓库
Awesome GitHub RepositoriesZero-Shot Temporal Models
Large-scale models capable of forecasting or classifying temporal data without task-specific fine-tuning.
Distinct from Zero-Shot Classification Models: Specializes zero-shot classification for temporal/time-series data specifically
Explore 2 awesome GitHub repositories matching artificial intelligence & ml · Zero-Shot Temporal Models. Refine with filters or upvote what's useful.
Awesome Zero-Shot Temporal Models GitHub Repositories
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- thuml/time-series-library
thuml/Time-Series-Library
12,494在 GitHub 上查看This PyTorch-based deep learning library provides a framework for analyzing and forecasting temporal data. It implements specialized architectures for time series forecasting, anomaly detection, data imputation, and classification. The project distinguishes itself through the inclusion of zero-shot inference capabilities, allowing large-scale temporal models to be evaluated on unseen datasets without requiring task-specific fine-tuning. The framework covers a broad range of analytical capabilities, including the recovery of missing values in incomplete datasets, the identification of irregul
Implements a framework for evaluating large-scale temporal models on unseen datasets without task-specific fine-tuning.
Python - nixtla/nixtla
Nixtla/nixtla
3,932在 GitHub 上查看Nixtla 是一个以基于 Transformer 的基础模型为中心的时序分析平台。它为预测和异常检测提供零样本推理，允许系统在无需模型重新训练的情况下预测新时序的未来值。 该项目专为大规模分析而设计，使用分布式推理扩展和预测并行化来处理数百万个数据序列。它支持微调适配以针对特定领域数据集调整预训练权重，并提供从本地执行和私有容器到作为 Snowflake 内存储过程集成等多种部署选项。 能力包括长周期和间歇性需求预测、假设场景分析以及预测不确定性量化。该系统还提供了一个完整的数据工程流水线，用于审计、清理和使用外生变量及基于日期的指标来丰富时序数据。 模型可靠性通过交叉验证回测、预测准确性验证以及用于超参数记录的实验跟踪来管理。
Predicts future values for new time series without requiring retraining by leveraging a pretrained foundation model.
Jupyter Notebookagentagentic-aianomaly-detection
