Sean Law - STUMPY: Modern Time Series Analysis with Matrix Profiles | SciPy 2024

Matrix profiles provide a powerful way to analyze time series data by finding similar patterns and anomalies without requiring prior knowledge or training data

STUMPY is a Python library that implements matrix profiles with high performance, supporting multi-CPU/GPU processing, streaming data, and distributed computing via Dask

Core capabilities include:

• Finding exact nearest neighbors and motifs in time series data
• Detecting anomalies and conserved behaviors
• Supporting multidimensional time series analysis
• Providing pan-matrix profiles for variable-length pattern matching

Key advantages:

• User-friendly API requiring minimal parameters
• Highly interpretable results based on Euclidean distance
• Scalable to large datasets (50M+ points)
• No need for data preprocessing like detrending
• 100% test coverage and battle-tested in production

Technical details:

• Uses sliding window Euclidean distance calculations
• Leverages FFT and computation reuse for efficiency
• Supports z-normalization of subsequences
• Minimal dependencies (NumPy, SciPy, Numba)
• Recent 15-20% performance improvements

Common use cases:

• Pattern discovery and motif detection
• Anomaly detection
• Time series joins and comparisons
• Clustering with matrix profile distance
• Exploratory data analysis

Active open source project with:

• 9 million+ downloads
• 3,000+ GitHub stars
• Regular releases and updates
• Support for latest Python/NumPy versions
• Extensive documentation and tutorials