82 repos across 4 sub-areas
Machine learning models and frameworks for time series prediction and forecasting, with emphasis on deep learning approaches. The cluster spans implementations in Python across multiple paradigms—from foundation models like Lag-Llama and Moirai that apply transformer and neural architectures to temporal data, to classical and hybrid forecasting methods. Tools here address both research-level model development and practical data science workflows for sequential prediction problems.
Time Series Forecasting with Transformers
25 repos
Machine learning models and research implementations for predicting time series data using transformer architectures and related deep learning techniques. The cluster centers on foundational transformer-based forecasting models like Lag-Llama and PatchTST, alongside IBM's Granite time series models, with supporting research codebases and experimental variants. Developers and researchers exploring this area will find model implementations, fine-tuning frameworks, and benchmarking tools for applying modern neural architectures to temporal prediction problems.
Time Series Forecasting with Transformers
24 repos
Libraries and models for predicting time series data using transformer-based deep learning architectures. The cluster centers on Chronos, a family of pretrained transformer models (tiny through large variants) designed for zero-shot time series forecasting across diverse datasets and domains. Resources here cover the practical application of modern NLP-inspired architectures to temporal prediction problems, including both the models themselves and supporting infrastructure for evaluation and deployment.
Time-Series Forecasting & Foundation Models
18 repos
Models and tools for forecasting temporal sequences using transformer-based and machine learning approaches. The cluster centers on foundation models like Moirai and Granite TimeSeries that apply large-scale pre-trained architectures to time-series prediction tasks, alongside supporting utilities and datasets. Resources here span model implementations, evaluation frameworks, and applications across different scales and configurations.
Time Series Forecasting & Deep Learning
15 repos
Python libraries and machine learning frameworks for predicting temporal sequences using deep learning models. This cluster covers end-to-end time series analysis—from foundational forecasting architectures (neural networks, attention mechanisms, probabilistic methods) to applied forecasting systems and datasets. Repositories include general-purpose forecasting libraries like DARTS, specialized probabilistic models for clinical predictions, and research implementations exploring neural architectures for sequential data.