Nixtla 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
cuDF is a GPU-accelerated dataframe library and data processing engine designed for manipulating and analyzing large tabular datasets. It provides a high-level API for executing filtering, joining, and aggregating operations directly on GPU hardware. The project integrates the Apache Arrow memory format to enable zero-copy data transfers and includes a just-in-time compiler for executing custom user-defined functions on the GPU. The library features specialized acceleration for existing workflows by redirecting standard Pandas dataframe calls and Polars query plans to a GPU backend. It also p
cuml is a GPU-accelerated machine learning library and framework that uses CUDA to accelerate tabular data preprocessing and model execution. It provides a suite of tools for training and deploying classification, regression, and clustering models on NVIDIA GPUs and GPU clusters. The library is designed for scalability, offering a distributed GPU machine learning environment that can spread computation and data across multiple hardware accelerators and nodes to handle datasets exceeding single-device memory. It mirrors standard estimator interfaces to allow the replacement of CPU-based models
statsforecast is a high-performance statistical time series forecasting library designed to generate point forecasts and prediction intervals. It functions as a distributed time series framework that utilizes a C-based forecasting engine and an automated model selector to identify and fit the optimal statistical model for every unique series in a dataset. The system also includes a time series anomaly detector to identify unusual data points by comparing observed values against probabilistic forecast intervals. The project is distinguished by its ability to handle massive-scale parallel forec
Stumpy 是一个 Python 库，专注于矩阵轮廓（matrix profile）算法的实现，用于可扩展的时间序列分析。它提供了一个框架，用于计算距离轮廓以识别时间序列数据中的重复模式和异常。
The main features of stumpy-dev/stumpy are: Motif Discovery, Matrix Profile Implementations, Pattern Identification, Time Series Anomaly Detection, Distributed Frameworks, Multidimensional Matrix Profiles, Distributed Time Series Computation, GPU-Accelerated Data Analysis.
Open-source alternatives to stumpy-dev/stumpy include: nixtla/nixtla — Nixtla is a time series analysis platform centered on a transformer-based foundation model. It provides zero-shot… rapidsai/cudf — cuDF is a GPU-accelerated dataframe library and data processing engine designed for manipulating and analyzing large… rapidsai/cuml — cuml is a GPU-accelerated machine learning library and framework that uses CUDA to accelerate tabular data… nixtla/statsforecast — statsforecast is a high-performance statistical time series forecasting library designed to generate point forecasts… sktime/sktime — sktime is a machine learning framework for time series analysis. It provides a unified toolkit for implementing time… unit8co/darts — Darts is a Python time series library designed for forecasting, anomaly detection, and the preprocessing of univariate…