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A Multipurpose Library for Synthetic Time Series Generation in Python
A simple but complete full-attention transformer with a set of promising experimental features from various papers
The C++ Core Guidelines are a set of tried-and-true guidelines, rules, and best practices about coding in C++
Python implementation for investigating the reliability of ICA estimates by clustering and visualization
Hackable and optimized Transformers building blocks, supporting a composable construction.
Python module to generate stochastic reduced order models (SROMs)
Python module for uncertainty quantification using a parallel sequential Monte Carlo sampler
Bounded-memory serverless distributed N-dimensional array processing
An extensible framework for linking databases and interactive views.
Make awesome display tables using Python.
🚀 Make your Python code fly at transonic speeds!
Superfast on-device object & vector database for Python
Performance-portable, length-agnostic SIMD with runtime dispatch
Python implementation of the Markov-Switching Multifractal model (MSM) of Calvet & Fisher (2004, 2008).
Chronos: Pretrained (Language) Models for Probabilistic Time Series Forecasting
List of papers, code and experiments using deep learning for time series forecasting
[ICML2024] Unified Training of Universal Time Series Forecasting Transformers
Generation and evaluation of synthetic time series datasets (also, augmentations, visualizations, a collection of popular datasets)
Immutable and statically-typeable DataFrames with runtime type and data validation
Expected tail loss portfolio optimisation in Python
Argilla is a collaboration tool for AI engineers and domain experts to build high-quality datasets
TensorRT-LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and build TensorRT engines that contain state-of-the-art optimizations to perform inference efficie…
