What does stumpy-dev/stumpy do?

Stumpy ist eine Python-Bibliothek für skalierbare Zeitreihenanalyse, die sich auf die Implementierung von Matrix-Profile-Algorithmen konzentriert. Sie bietet ein Framework zur Berechnung von Distanzprofilen, um wiederkehrende Muster und Anomalien innerhalb von Zeitreihendaten zu identifizieren.

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 - Matrix-Profile für Zeitreihen berech…

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.

Open-Source-Alternativen zu Stumpy

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nixtla/nixtla
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nixtla/statsforecast
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Stumpy - Matrix-Profile für Zeitreihen berech… | Awesome Repos

stumpy-devstumpy

Stumpy

Features

Open-Source-Alternativen zu Stumpy

Nixtla/nixtla

rapidsai/cudf

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Nixtla/statsforecast

Frequently asked questions

Star-Verlauf

Open-Source-Alternativen zu Stumpy

Nixtla/nixtla

rapidsai/cudf

rapidsai/cuml

Nixtla/statsforecast

Frequently asked questions