What does stumpy-dev/stumpy do?

Stumpy es una librería de Python para análisis de series temporales escalable centrada en la implementación de algoritmos de perfil de matriz (matrix profile). Proporciona un framework para calcular perfiles de distancia para identificar patrones repetitivos y anomalías dentro de datos de series temporales.

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 - calcular perfiles de matriz para ser…

Stumpy - calcular perfiles de matriz para ser… | 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.

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Ver las 30 alternativas a Stumpy

stumpy-devstumpy

Stumpy

Features

Alternativas open-source a Stumpy

Nixtla/nixtla

rapidsai/cudf

rapidsai/cuml

Nixtla/statsforecast

Frequently asked questions

Historial de estrellas

Alternativas open-source a Stumpy

Nixtla/nixtla

rapidsai/cudf

rapidsai/cuml

Nixtla/statsforecast

Frequently asked questions