scalable analysis of images and time series
الميزات الرئيسية لـ thunder-project/thunder هي: Time Series Analysis, Python Geospatial Libraries, Python Toolboxes.
تشمل البدائل مفتوحة المصدر لـ thunder-project/thunder: spro/practical-pytorch — Practical PyTorch is a collection of deep learning tutorials and guides focused on implementing recurrent neural… alan-turing-institute/sktime — sktime is a machine learning framework designed for time series analysis. It provides a unified interface for… albertoalmuinha/garchmodels — The Tidymodels Extension for GARCH models. alexiosg/rmgarch — Multivariate GARCH Models. alexiosg/rugarch — Univariate GARCH models in R. adbeda/ts-raster — Extract and analyze space-time-series characteristics from raster data.
Practical PyTorch is a collection of deep learning tutorials and guides focused on implementing recurrent neural networks. The project provides practical code for building sequence models and sequence-to-sequence architectures using the PyTorch framework. The repository covers the implementation of models for neural machine translation, character-level text generation, and text classification. It includes examples for transforming input sequences into output sequences for machine translation and synthesizing new text. The project also extends to sequence data prediction and time series analy
sktime is a machine learning framework designed for time series analysis. It provides a unified interface for performing time series forecasting, classification, and anomaly detection, integrating these capabilities into a standardized toolkit compatible with the scikit-learn API. The framework allows for the construction of complex analysis workflows through model pipelining and ensemble-based aggregation. It uses adapter-based integration to wrap external time series libraries, providing a single entry point for diverse algorithmic implementations. Its capabilities cover temporal data tran
The Tidymodels Extension for GARCH models
Extract and analyze space-time-series characteristics from raster data.