Projects sharing features with Statsforecast

GluonTS is a probabilistic time series library and deep learning forecasting framework. It provides a toolkit for building, training, and evaluating neural network architectures that predict future values as probability distributions to quantify uncertainty. The project distinguishes itself by supporting zero-shot forecasting and integrating diverse modeling approaches, including deep probabilistic neural networks and wrappers for external statistical libraries such as Prophet and R forecast. It implements specialized architectural primitives like causal convolutions and invertible residual n

Nixtla is a time series analysis platform centered on a transformer-based foundation model. It provides zero-shot inference for forecasting and anomaly detection, allowing the system to predict future values for new time series without requiring model retraining. The project is designed for large-scale analysis, using distributed inference scaling and forecast parallelization to process millions of data series. It supports fine-tuning adaptation to adjust pretrained weights for domain-specific datasets and offers deployment options ranging from local execution and private containers to integr

PyTorch Forecasting is a deep learning framework designed for building and training neural network architectures specifically for time series forecasting. It serves as a comprehensive toolkit for implementing autoregressive models, multi-horizon forecasting, and probabilistic prediction intervals using PyTorch tensors. The library distinguishes itself through a probabilistic forecasting toolkit that generates prediction intervals and quantile forecasts using both parametric and non-parametric distributions. It further provides a neural network model optimizer for automated hyperparameter tuni

GluonTS is a framework for probabilistic time series forecasting, designed to predict future values as probability distributions with confidence intervals. It supports both traditional model training and zero-shot forecasting, where pretrained models generate predictions for new series without additional training. The project distinguishes itself by integrating a wide variety of forecasting approaches into a unified workflow. This includes deep learning architectures such as recurrent neural networks and causal convolutions, as well as the integration of external statistical models, the Proph

TimesFM is a time series foundation model designed to generalize across diverse temporal datasets for forecasting and anomaly detection. It functions as a pretrained model for predicting future values in univariate time series data, eliminating the need for manual training from scratch. The project includes a framework for adapting pretrained weights to specific datasets using low-rank adaptation to improve accuracy. It also provides specialized capabilities for integrating time-series predictions as tools within autonomous AI agent architectures and automated workflows. The system supports

Kats is a time series analysis framework and library providing tools for statistical characterization, anomaly detection, and trend forecasting. It functions as a toolkit for predicting future values based on historical data and identifying irregular patterns or structural change points within temporal sequences. The project includes a temporal feature extraction tool to calculate descriptive statistics and characteristics that summarize time series behavior. It also provides a system for model hyperparameter tuning using self-supervised learning to improve the scale and generalization of pre

This PyTorch-based deep learning library provides a framework for analyzing and forecasting temporal data. It implements specialized architectures for time series forecasting, anomaly detection, data imputation, and classification. The project distinguishes itself through the inclusion of zero-shot inference capabilities, allowing large-scale temporal models to be evaluated on unseen datasets without requiring task-specific fine-tuning. The framework covers a broad range of analytical capabilities, including the recovery of missing values in incomplete datasets, the identification of irregul

Merlion is a time series machine learning framework designed for anomaly detection and forecasting. It provides a unified interface for implementing and applying various statistical and machine learning models to temporal data streams. The project includes a benchmarking dashboard that allows for the visual testing and evaluation of models against historical ground truth datasets. This web interface enables the experimentation of different models on custom datasets without manual coding. The framework covers capabilities for identifying outliers, predicting future time series values, and mea

This is a deep learning framework for predicting future values in sequential data using PyTorch architectures. It provides a toolkit for long-horizon and probabilistic time series prediction, incorporating a data pipeline to convert tabular dataframes into sequences for supervised deep learning training. The library utilizes a training wrapper to scale model execution across CPUs and GPUs. It supports the generation of probability distributions for future outcomes instead of single point estimates to quantify prediction uncertainty. The framework includes capabilities for implementing foreca

NuPIC is a machine learning framework that implements Hierarchical Temporal Memory (HTM) theory, a neuroscience-inspired approach to artificial intelligence. It models principles of the neocortex to build systems capable of learning patterns from streaming data, performing sequence prediction, and detecting anomalies in real-time data streams. The framework is built around a Cortical Learning Algorithm that combines spatial pooling and temporal memory to process streaming input. It uses Sparse Distributed Representations to encode input patterns, a Spatial Pooler to convert dense input into s

sktime is a machine learning framework for time series analysis. It provides a unified toolkit for implementing time series classification, forecasting, and anomaly detection using standardized machine learning interfaces. The library serves as a collection of tools for assigning categorical labels to temporal sequences, predicting future values based on historical patterns, and identifying outliers or unusual patterns within temporal data. The framework includes capabilities for panel-data handling and pipeline-based transformations. It utilizes a unified API wrapper and plugin-based model

AutoGluon is an automated machine learning framework designed to optimize model selection and hyperparameter tuning across tabular, text, image, and time series data. It functions as an ensemble learning library and a tabular data prediction engine, aiming to build high-accuracy predictive models without manual algorithm selection. The framework integrates multimodal machine learning pipelines that combine disparate data types into a single representation using specialized encoders. It also includes a probabilistic time series forecaster that fits multiple statistical and deep learning models

Neural Prophet is a PyTorch-based time series forecasting library designed for interpretable machine learning. It serves as a decomposition framework that breaks signals into constituent parts such as autoregressive effects, piecewise linear trends, and Fourier-based seasonality to predict future values. The project distinguishes itself by combining neural networks with traditional algorithms to produce forecasts that explain underlying trend drivers. It features a global time series modeling approach, allowing a single model to be trained across multiple simultaneous series to share learned

PyCaret is a Python AutoML platform and MLOps lifecycle manager designed to automate machine learning workflows. It functions as a low-code environment that leverages a scikit-learn native engine to execute preprocessing, training, and evaluation for tabular data. The platform distinguishes itself as an LLM-powered ML copilot, using large language model agents to analyze datasets, design experiment configurations, and explain model results. It also serves as a Kubernetes ML orchestrator and model registry, enabling the versioning of trained pipelines and their promotion to production API endp

PaddleX is a PaddlePaddle-based framework for building, deploying, and fine-tuning AI model pipelines, with pre-built support for computer vision, OCR, document analysis, and time series tasks. It offers a toolkit of ready-to-use pipelines for image classification, object detection, segmentation, and pose estimation, alongside an end-to-end OCR document analysis pipeline that extracts text, tables, formulas, and layout information. The platform also includes a dedicated time series forecasting pipeline for analyzing historical data to detect anomalies, classify patterns, and predict future val

SparkInternals is a technical reference and architecture guide detailing the internal design and implementation of the Apache Spark distributed computing engine. It serves as a study of big data engine analysis, focusing on how the system manages cluster execution and the interaction between driver nodes, executors, and workers. The project provides a detailed breakdown of how logical plans are converted into physical execution stages. It specifically analyzes the mechanics of data shuffle operations, memory management, and the coordination of distributed job scheduling. The documentation co

PyMC is a Bayesian probabilistic programming framework used for building probabilistic models and performing Bayesian inference. It provides a probabilistic graphical model library for specifying random variables, priors, and likelihood functions, supported by an MCMC sampling engine and variational inference tools to estimate posterior distributions. The framework features a GPU-accelerated inference backend that compiles models into machine code to increase execution speed. It utilizes a backend-agnostic tensor execution model and just-in-time graph compilation to optimize the computation o

Prophet is a predictive analytics framework and time series regression library designed for forecasting future values. It uses additive models to fit non-linear growth and periodic seasonal patterns, providing tools for producing forecasts with integrated error measurement. The project handles multiple seasonalities and holiday effects to improve accuracy for periodic data. It supports the integration of external regressors and manages data irregularities, such as missing data and outliers, to maintain prediction stability. The framework covers a broad range of analysis capabilities, includi

Open-Meteo is a free, open-source REST API that delivers weather forecasts, historical weather records, air quality data, marine conditions, solar radiation, and geocoding services. It aggregates data from dozens of freely licensed global and regional numerical weather models, making a broad set of environmental information accessible through a single API endpoint. The API uses a multi-model query router that automatically selects the most suitable model for any location, time range, and variable requested, while also allowing manual model choice. It handles unit and timezone conversion on th

Chronos-forecasting is a zero-shot time series forecasting framework based on a pretrained large language model. It enables the prediction of future values across diverse datasets without requiring task-specific training or optimization. The system functions as a probabilistic forecasting tool, producing multiple future trajectories and quantile forecasts to quantify uncertainty and potential prediction errors. It incorporates exogenous covariate integration to merge external variables and historical context into the input stream for increased precision. The project includes utilities for sy

This project is a machine learning educational curriculum and learning platform delivered through interactive Jupyter Notebooks. It serves as a comprehensive guide for mastering the Python data science toolkit, providing structured tutorials for numerical computing, tabular data manipulation, and statistical visualization. The curriculum includes specific implementation guides for Scikit-Learn and a practical course on TensorFlow for constructing, training, and deploying neural networks and computer vision models. It covers the end-to-end process of building predictive models, from initial pr

QuantResearch is a quantitative research framework and specialized toolkit for algorithmic simulation, financial time-series analysis, and systematic trading. It provides an event-driven backtesting environment for validating strategies against historical tick and bar data, alongside a dedicated portfolio optimization engine for calculating asset weights and risk metrics. The project distinguishes itself through a machine learning finance toolkit that implements recurrent neural networks for price prediction and reinforcement learning for derivative pricing. It also features advanced statisti

tsai is a deep learning library for time series classification, regression, and forecasting. Built on PyTorch and fastai, it provides a framework for assigning labels to sequential data, predicting future values in univariate or multivariate sequences, and training representations on unlabeled data through self-supervised learning. The library distinguishes itself with specialized temporal engineering and scaling capabilities. It includes tools for cyclical temporal encoding to capture seasonal patterns and online window slicing to process datasets larger than available memory. It also suppor

sktime is a machine learning framework designed for time series analysis. It provides a unified interface for performing time series forecasting, classification, and anomaly detection, integrating these capabilities into a standardized toolkit compatible with the scikit-learn API. The framework allows for the construction of complex analysis workflows through model pipelining and ensemble-based aggregation. It uses adapter-based integration to wrap external time series libraries, providing a single entry point for diverse algorithmic implementations. Its capabilities cover temporal data tran

This project is a suite of machine learning and statistical tools designed for stock price prediction, financial time series forecasting, and the execution of algorithmic trading strategies. It provides a collection of deep learning and statistical models used to forecast asset prices and market trends. The system includes a market scenario simulator that uses Monte Carlo sampling to generate potential price paths and estimate financial risk. It further features a portfolio optimization tool for calculating asset distributions to maximize returns based on historical volatility, as well as a m

This repository is a collection of reference implementations, templates, and sample galleries for building and integrating machine learning models within the .NET ecosystem. It provides a set of practical demonstrations for implementing machine learning workflows using the ML.NET framework. The project emphasizes the integration of pre-trained models via the Open Neural Network Exchange format, allowing the execution of external machine learning logic within managed applications. It includes specific examples for loading and executing these standardized models to ensure cross-platform compati

Brain.js is a JavaScript neural network library for building, training, and running machine learning models in the browser or Node.js. It provides implementations for several network types, including feedforward networks, recurrent neural networks for time series forecasting, and autoencoders for data compression and denoising. The library features WebGL-based GPU acceleration to increase the speed of neural network computations on the graphics processor. It also includes a visualization tool that generates SVG images to represent the topology and layers of a feedforward network. The framewo