Projects sharing features with Darts

statsforecast is a high-performance statistical time series forecasting library designed to generate point forecasts and prediction intervals. It functions as a distributed time series framework that utilizes a C-based forecasting engine and an automated model selector to identify and fit the optimal statistical model for every unique series in a dataset. The system also includes a time series anomaly detector to identify unusual data points by comparing observed values against probabilistic forecast intervals. The project is distinguished by its ability to handle massive-scale parallel forec

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

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

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

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

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

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

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

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

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

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

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

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

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 collection of predictive models and quantitative tools for stock price forecasting. It implements a variety of machine learning architectures, including generative adversarial networks, long short-term memory networks, and language models for financial analysis. The system distinguishes itself by combining time-series forecasting with natural language processing to convert financial news into numerical sentiment scores. It also incorporates synthetic market data generation and automated hyperparameter optimization using Bayesian and reinforcement learning methods to reduce p

DataFrame is a C++ tabular data library and manipulation engine designed for managing heterogeneous data in contiguous memory. It functions as a statistical analysis framework and time series analysis toolkit, providing the means to store, index, and transform multidimensional datasets. The project distinguishes itself through a high-performance execution model that utilizes column-major storage, SIMD-aligned memory allocation, and a thread-pool for parallel computations. It employs a visitor-based algorithm dispatch system and policy-driven transformations to decouple data processing logic f

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

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

tsfresh is an automated feature engineering tool and library designed to extract statistical characteristics from raw time series data. It transforms sequential data into tabular datasets, converting time series into a flat format where each row represents a unique entity and columns represent extracted features. The project distinguishes itself through a parallel data processing framework that distributes heavy computational workloads across multiple CPU cores. It also implements hypothesis-based feature selection to identify the most predictive characteristics and filter out irrelevant ones

This project is a time series forecasting model implemented in Python and Keras. It is a deep learning system designed to predict future values in sequential datasets by training long short-term memory neural networks on historical numerical data. The implementation focuses on sequential data analysis, specifically applying these models to financial market prediction to forecast price movements and trends. The architecture covers data preprocessing through min-max feature scaling and sliding-window transformations. It utilizes recurrent neural network cells with gating mechanisms for long-te

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

This project is a comprehensive collection of practical code examples and implementation libraries for machine learning. It provides a wide array of reference materials for building supervised, unsupervised, and reinforcement learning algorithms. The repository serves as a multi-domain resource, featuring specific implementation suites for financial AI, Bayesian statistical modeling, and deep learning architectures. It includes a framework for training intelligent agents using policy gradients and actor-critic models, as well as practical guides for fine-tuning transformers and utilizing larg

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

Lag-llama is a probabilistic machine learning foundation model designed for time series forecasting. It generates predictive distributions and uncertainty bounds for sequential data across arbitrary frequencies by leveraging pre-trained foundational weights. The system supports zero-shot transfer inference, allowing it to predict future values on entirely new and unseen datasets without requiring prior retraining. It achieves this by combining generalized representations from foundational training with adjustable context lengths, where historical context lengths and lagged feature values feed

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

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

This project is a deep learning tutorial series and educational curriculum designed to teach PyTorch fundamentals. It serves as a structured training guide for mastering neural network architecture, automatic differentiation, and the use of tensors and dynamic computation graphs. The curriculum focuses on practical implementations, specifically guiding the development of recommendation systems, advertising models, and interest networks to predict user preferences. It also provides instructional content for time series forecasting and processing sequential data. The material covers a broad ra