What does stumpy-dev/stumpy do?

Stumpy est une bibliothèque Python pour l'analyse de séries temporelles évolutive centrée sur l'implémentation d'algorithmes de profil de matrice. Elle fournit un framework pour calculer des profils de distance afin d'identifier des modèles répétitifs et des anomalies au sein des données de séries temporelles.

What are the main features of stumpy-dev/stumpy?

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.

What are some open-source alternatives to stumpy-dev/stumpy?

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…

Stumpy - calculer des profils de matrice pour…

Stumpy - calculer des profils de matrice pour… | Awesome Repos

Features

Motif Discovery - Implements matrix profile algorithms to discover recurring patterns and motifs within long time series datasets.
Matrix Profile Implementations - Implements matrix profile algorithms for calculating distance profiles to identify repeating patterns and discords.
Pattern Identification - Identifies repeating patterns and anomalies by calculating the distance between every subsequence and its nearest neighbor.
Time Series Anomaly Detection - Identifies discords and novelty patterns that deviate significantly from the rest of the time series data.
Distributed Frameworks - Ships a scalable framework using Dask to parallelize heavy matrix profile computations across machines.
Multidimensional Matrix Profiles - Computes matrix profiles for multidimensional data to identify similar patterns across different variables.
Distributed Time Series Computation - Provides a scalable system to parallelize matrix profile computations across GPU hardware and Dask clusters.
GPU-Accelerated Data Analysis - Offloads complex matrix calculations to GPU hardware to significantly reduce processing time for large datasets.
Incremental Matrix Profiles - Calculates matrix profiles incrementally as new data arrives to monitor time series in real time.
GPU-Accelerated Processing - Offloads complex matrix calculations to GPU hardware to reduce processing time for large datasets.
Time Series Analysis Libraries - Provides a comprehensive Python library for analyzing time series data using matrix profile algorithms.
Multidimensional Analysis - Implements multidimensional analysis to discover repeating patterns and motifs across multiple concurrent data streams.
Multidimensional Motif Discovery - Provides capabilities to find repeating patterns across multiple concurrent data streams simultaneously.
Streaming Analysis - Offers incremental computation to monitor time series in real time as new data points arrive.
Distance Profile Computations - Calculates the distance between a specific query subsequence and every subsequence in a time series using the MASS algorithm.
Matrix Profile Computation - Calculates distances between all subsequences of a time series using z-normalization and hardware acceleration.
Chain Tracing - Discovers extended repeating patterns by computing anchored and unanchored chains of similar subsequences.
Distributed Computing - Parallelizes distance profile calculations across multiple processors or servers to handle larger workloads.
Dask Integrations - Distributes matrix profile computations across a cluster of machines using Dask integration.
Chain Extraction - Analyzes sequential behavior by identifying temporally ordered sets of subsequence patterns.
Time Series Segmenters - Detects regime changes and behavioral shifts by dividing time series into semantically distinct segments.
Query Sequence Matching - Locates all occurrences of a specific query subsequence within a time series that fall below a defined distance threshold.
Multidimensional Analyzers - Provides a framework for discovering motifs and patterns across multiple concurrent data streams simultaneously.
Similarity Measurements - Computes the distance between two time series based on the overlap of their shared subsequences.
Pan Matrix Profiles - Analyzes patterns of varying lengths by calculating matrix profiles across multiple different window sizes.
Consensus Motif Identification - Identifies the most central conserved pattern shared across multiple different time series.
Time Series Pattern Matching - Finds similar shapes by searching for specific motifs or conserved patterns across one or multiple time series.
Time Series Analysis - Scalable library for modern time series matrix profile analysis.

Alternatives open source à Stumpy

Projets open source similaires, classés selon le nombre de fonctionnalités partagées avec Stumpy.

nixtla/nixtla
Nixtla/nixtla
3,932Voir sur GitHub
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
Jupyter Notebookagentagentic-aianomaly-detection
Voir sur GitHub3,932
rapidsai/cudf
rapidsai/cudf
9,672Voir sur GitHub
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
C++
Voir sur GitHub9,672
rapidsai/cuml
rapidsai/cuml
5,209Voir sur GitHub
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
Python
Voir sur GitHub5,209
nixtla/statsforecast
Nixtla/statsforecast
4,809Voir sur GitHub
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
Python
Voir sur GitHub4,809

Voir les 30 alternatives à Stumpy

stumpy-devstumpy

Stumpy

Features

Alternatives open source à Stumpy

Nixtla/nixtla

rapidsai/cudf

rapidsai/cuml

Nixtla/statsforecast

Frequently asked questions

Historique des stars

Alternatives open source à Stumpy

Nixtla/nixtla

rapidsai/cudf

rapidsai/cuml

Nixtla/statsforecast

Frequently asked questions