Pathway is a high-performance live data processing framework built on top of timely dataflow and differential dataflow, providing a Python API for defining streaming and batch data pipelines. It is designed for real-time ETL, event processing, AI/ML pipelines (including RAG), and incremental computation with strong consistency guarantees. The architecture consists of a Rust core engine that handles dataflow execution, scheduling, persistence, and connectors, while the Python layer provides a declarative API for table operations, UDFs, and integrations with external systems like Kafka, databases, vector stores, and cloud storage. Key entry points include the `pathway` CLI, the Python `pw` module, and the Rust engine library. Data flows through connectors (sources/sinks), transformation operators (joins, groupby, windowing, etc.), and optional persistence for fault tolerance. External dependencies include Kafka, S3, PostgreSQL, various vector databases, and LLM services.
sktime is a large, actively developed Python library that provides a unified scikit-learn-style estimator interface for time series machine learning. It is not a single-purpose application or service; it is a framework with a central registry, a datatype/mtype conversion system, tag-based estimator metadata, and a sophisticated automated test suite that generates per-estimator tests from scenarios. The dominant character is application/library, though it has strong research-data aspects: bundled datasets, benchmark suites, and algorithm implementations drawn from the time-series literature (forecasting, classification, clustering, change-point detection, transformations, alignment, distances/kernels, and parameter estimation).
This repository provides the official code, datasets, and checkpoints for the Timer family of large time series foundation models (Timer, Timer-XL, Sundial) from Tsinghua University. It implements a decoder-only Transformer architecture pre-trained on the Unified Time Series Dataset (UTSD) using the single-series sequence (S3) format. The code supports fine-tuning and evaluation on three downstream tasks: forecasting, segment-level imputation, and predictive anomaly detection. Data loading modules handle multiple time series formats (CSV, NPZ, TXT) with flexible windowing and scaling. The repository includes shell scripts for reproducible experiments across multiple datasets and data scarcity ratios. It also provides HuggingFace integration for zero-shot forecasting without training. While primarily a research codebase rather than a production application, it is well-structured with clean module separation and reusable components.
This repository is a comprehensive, curated list of anomaly detection (outlier detection) resources, maintained by the author of the PyOD library. It aggregates books, academic papers, tutorials, open-source toolkits, datasets, and relevant conferences/journals, covering tabular, time-series, graph, and text data modalities. The repository also includes two utility scripts: a downloader for paper PDFs and a robust URL checker for verifying links in the documentation. While the core value is the curated resource list itself, the utility scripts demonstrate clean engineering practices and are reusable for similar link-checking and batch download tasks. This is not a software application or data pipeline; it is a reference catalog for researchers and practitioners.
This repository provides implementations of the Dynamic Time Warping Barycenter Averaging (DBA) algorithm in multiple programming languages (Java, Python, MATLAB, and Cython). The DBA algorithm computes the average of a set of time series under Dynamic Time Warping (DTW) by iteratively aligning each series to the current average and updating the average as the mean of aligned points. The repository includes the core algorithm, a multivariate extension, and versions with warping window constraints. The primary implementation is in Python (DBA.py), which is self-contained and requires only NumPy. The code is well-structured and demonstrates the iterative DTW-based averaging process, which is a non-trivial approach to time series averaging.