2 dépôts
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
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Awesome Zero-Shot Temporal Models GitHub Repositories
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- thuml/time-series-library
thuml/Time-Series-Library
12,494Voir sur GitHubThis 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
3,932Voir sur GitHubNixtla is a time series analysis platform centered on a transformer-based foundation model. It provides zero-shot inference for forecasting and anomaly detection, allowing the system to predict future values for new time series without requiring model retraining. The project is designed for large-scale analysis, using distributed inference scaling and forecast parallelization to process millions of data series. It supports fine-tuning adaptation to adjust pretrained weights for domain-specific datasets and offers deployment options ranging from local execution and private containers to integr
Predicts future values for new time series without requiring retraining by leveraging a pretrained foundation model.
Jupyter Notebookagentagentic-aianomaly-detection
