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).
StatsForecast is a production-grade Python library for large-scale statistical time series forecasting. It provides a scikit-learn-like API (fit/predict) with over 30 models including AutoARIMA, AutoETS, AutoCES, AutoTheta, TBATS, MSTL, GARCH, and intermittent demand models. The library achieves high performance through a hybrid architecture: core algorithms are implemented in C++ using Eigen and pybind11, while Python handles orchestration, model selection, and validation. It supports distributed computing via Fugue (Spark, Dask, Ray) and multiprocessing, enabling forecasting on millions of time series. The library also includes conformal prediction intervals, cross-validation, simulation, and feature engineering utilities. While primarily an application library, the repository has a strong research component with extensive benchmarking experiments against R's forecast package, pmdarima, Prophet, NeuralProphet, and Amazon Forecast. These experiments are well-documented with reproducible environments (Docker, conda, Makefiles) and use public datasets (M3, M4, M5, Tourism, ERCOT). The codebase is actively maintained with CI/CD across multiple OS/Python versions, automated wheel building, and documentation pipelines using Quarto and Mintlify. The architecture is clean and modular: models inherit from a common base class (_TS), distributed backends implement a ParallelBackend interface, and C++ code is separated into submodules. Notable reusable components include a generic Nelder-Mead optimizer, a unified distribution framework, and a stepwise ARIMA search algorithm ported from R. The library is well-tested with comprehensive unit and integration tests.
Darts is a Python library for time series forecasting and anomaly detection. It provides a unified API for a wide range of models, from classical statistical methods (ARIMA, Exponential Smoothing) to deep learning architectures (N-BEATS, TFT, TiDE, Transformer) and foundation models (Chronos, TimesFM, PatchTST-FM). The library also includes data preprocessing transformers (scaling, missing value filling, Box-Cox, DTW), anomaly detection scorers and detectors, explainability tools (SHAP, TFT attention), and comprehensive evaluation metrics. The architecture is modular: core TimeSeries data structure, model registry with lazy imports, transformer pipeline, and safe serialization. External dependencies include pandas, numpy, scikit-learn, PyTorch, PyTorch Lightning, and many optional packages for specific models. The codebase is well-tested with extensive unit and integration tests, and follows good engineering practices with CI/CD, dependency audits, and semantic versioning.
FlexMeasures is a Flask-based energy flexibility management platform that ingests time-series sensor data, forecasts future values, schedules flexible assets (batteries, heat pumps, industrial processes) using linear optimization, and reports on performance. It provides a REST API (v3.0), a web UI with interactive charts, a CLI for data management, and background job queues (RQ) for forecasting, scheduling, and automation. The architecture is a multi-tenant web application with PostgreSQL, Redis, and optional Sentry monitoring. It features a plugin system for custom data generators (forecasters, schedulers, reporters) and automation handlers. The codebase is well-tested with extensive unit and integration tests, and follows modern Python practices (type hints, Marshmallow schemas, Click CLI).
Merlion is a mature Python library for time series intelligence, specializing in anomaly detection and forecasting. It provides a comprehensive, modular ecosystem including model zoo (classical, tree-based, deep learning, ensemble, AutoML), composable transforms, post-processing chains, evaluation frameworks, and a Dash-based interactive dashboard. The architecture follows a clean layered design: data loading via `ts_datasets`, core `TimeSeries` abstraction, `ModelBase` hierarchy with config-driven instantiation, invertible transforms, and pluggable post-rules. It integrates with Spark for distributed inference and provides benchmarks against standard datasets (NAB, M4, UCR). Though primarily a library (application), it includes research-oriented components such as state-of-the-art transformer architectures (Autoformer, Informer) and Bayesian change point detection, making it a hybrid tool suitable for both production and research.
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.
pytorch-forecasting is a deep learning library for time series forecasting, providing implementations of state-of-the-art models such as Temporal Fusion Transformer, N-BEATS, N-HiTS, DeepAR, PatchTST, TiDE, TimeXer, and many others. Built on PyTorch and PyTorch Lightning, it offers a complete pipeline from data preprocessing (TimeSeriesDataSet, encoders, samplers) through model training (with GPU support, callbacks, hyperparameter tuning via optuna) to prediction and evaluation (point, quantile, and distributional metrics). The library supports multivariate forecasting, static and time-varying covariates, categorical features, and multiple targets. A major v2 refactoring is underway, introducing a new data module layer and simplified package interface, though the v2 API is explicitly marked as experimental and unstable. The codebase is well-tested with automated integration tests across all models and metrics, and employs a composition pattern for metrics (supporting weighting, aggregation, and distribution losses) and a layered architecture for models (base classes, package containers, building blocks).
Kronos is a foundation model for financial K-line (OHLCV) time series data, accepted at AAAI 2026. It introduces a novel two-stage architecture: a BinarySphericalQuantizer tokenizer that converts continuous multi-dimensional market data into hierarchical discrete tokens, followed by an autoregressive decoder-only Transformer (with RoPE, RMSNorm, hierarchical embedding, and dependency-aware layers) pre-trained on data from 45+ global exchanges. The repository provides a complete research pipeline including model definition, tokenizer training, predictor fine-tuning (via Qlib or custom CSV), backtesting, and inference through multiple interfaces (Python API, CLI examples, a Tkinter GUI, and a Flask web UI). While the core model and tokenizer demonstrate strong research craftsmanship, the codebase exhibits signs of rapid prototyping: many example scripts with overlapping functionality, hardcoded paths, AI-generated comments, and prediction outputs that sometimes degenerate to constant values or numerical instability, indicating the model is still in an experimental stage. The repository is hybrid in nature—primarily a research project with application wrappers for demonstration and evaluation.
This repository implements FiveThirtyEight's NFL Elo forecasting model and evaluation framework. The data lineage flows from two raw CSV sources: `data/nfl_games.csv` containing historical game results (1920-present) with pre-computed Elo win probabilities, and `data/initial_elos.csv` providing starting Elo ratings for each franchise. The `forecast.py` module implements the core Elo engine: it initializes team ratings, applies season-to-season reversion (retaining 2/3 of prior rating, reverting 1/3 toward 1505), incorporates home-field advantage (65 Elo points), and updates ratings post-game using a margin-of-victory multiplier (log(pd+1) * 2.2/(elo_diff*0.001+2.2)). Special hardcoded reversions handle franchise relocations/expansions. The `util.py` module handles CSV parsing with type coercion and evaluates forecasts using a Brier-score-based points system (25 - 100*brier, doubled for playoffs). The `eval.py` script orchestrates the full pipeline: load games, generate forecasts, evaluate against FiveThirtyEight's baseline. The repo answers the research question: 'Can alternative forecasting models beat FiveThirtyEight's Elo model on historical NFL data?'
This repository is a design portfolio and technical showcase for a solar photovoltaic GIS prospecting and forecasting platform. It does not contain a runnable application; instead, it provides architectural documentation (docs/ARCHITECTURE.md, docs/MODULES.md, docs/AI_FORECASTING.md) and three well-crafted code snippets that demonstrate the engineering patterns behind map-based lead identification, AI roof detection service, and PV generation forecasting. The snippets illustrate professional-level software design: input validation, security boundaries, model routing, fallback strategies, and business logic normalization. The repository serves as a reference for architects and engineers evaluating similar systems, but it is explicitly non-reusable for commercial purposes due to the license restrictions.