Search Results for "forecasting"
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Showing 111 open source projects for "forecasting"
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The All-In-One Google Workspace Management Tool for IT AdminsgPanel by Promevo streamlines administration, security, and user management, giving organizations full control over their Google Workspace.
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Securden Windows Privilege ManagerRemove local administrator rights on Windows servers and endpoints. Seamlessly elevate applications for standard users. Grant time-limited rights on-demand. Control application usage by remote employees through whitelisting and blacklisting.
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PyTorch Forecasting
Time series forecasting with PyTorch
PyTorch Forecasting aims to ease state-of-the-art time series forecasting with neural networks for both real-world cases and research alike. The goal is to provide a high-level API with maximum flexibility for professionals and reasonable defaults for beginners. A time series dataset class that abstracts handling variable transformations, missing values, randomized subsampling, multiple history lengths, etc. -
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Chronos Forecasting
Pretrained (Language) Models for Probabilistic Time Series Forecasting
Chronos is a family of pretrained time series forecasting models based on language model architectures. A time series is transformed into a sequence of tokens via scaling and quantization, and a language model is trained on these tokens using the cross-entropy loss. Once trained, probabilistic forecasts are obtained by sampling multiple future trajectories given the historical context. -
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StatsForecast
Fast forecasting with statistical and econometric models
StatsForecast is a Python library for time-series forecasting that delivers a suite of classical statistical and econometric forecasting models optimized for high performance and scalability. It is designed not just for academic experiments but for production-level time-series forecasting, meaning it handles forecasting for many series at once, efficiently, reliably, and with minimal overhead. -
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mlforecast
Scalable machine learning for time series forecasting
mlforecast is a time-series forecasting framework built around machine-learning models, designed to make forecasting both efficient and scalable. It lets you apply any regressor that follows the typical scikit-learn API, for example, gradient-boosted trees or linear models, to time-series data by automating much of the messy feature engineering and data preparation. -
Powerful Website Security | Continuous Web Threat PlatformReflectiz is a comprehensive web exposure management platform that helps organizations proactively identify, monitor, and mitigate security, privacy, and compliance risks across their online environments. Designed to address the growing complexity of modern websites, Reflectiz provides full visibility and control over first, third, and even fourth-party components, such as scripts, trackers, and open-source libraries that often evade traditional security tools.
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forecast
Forecasting Functions for Time Series and Linear Models
The forecast package is a comprehensive R package for time series analysis and forecasting. It provides functions for building, assessing, and using univariate forecasting models (e.g. ARIMA, exponential smoothing, etc.), tools for automatic model selection, diagnostics, plotting, forecasting future values, etc. It's widely used in statistics, economics, business forecasting, environmental science, etc. Exponential smoothing state space models (ETS) including seasonal components. ... -
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NeuralForecast
Scalable and user friendly neural forecasting algorithms.
...There is a shared belief in Neural forecasting methods' capacity to improve forecasting pipeline's accuracy and efficiency. Unfortunately, available implementations and published research are yet to realize neural networks' potential. They are hard to use and continuously fail to improve over statistical methods while being computationally prohibitive. For this reason, we created NeuralForecast, a library favoring proven accurate and efficient models focusing on their usability. -
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TimesFM
Pretrained time-series foundation model developed by Google Research
...Newer releases emphasize expanded context handling and more flexible forecasting outputs, including quantile forecasting so users can get uncertainty estimates rather than only point predictions. The repository also documents how model versions evolved, with newer variants focusing on efficiency and longer context windows while maintaining forecasting quality. -
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Google DeepMind GraphCast and GenCast
Global weather forecasting model using graph neural networks and JAX
GraphCast, developed by Google DeepMind, is a research-grade weather forecasting framework that employs graph neural networks (GNNs) to generate medium-range global weather predictions. The repository provides complete example code for running and training both GraphCast and GenCast, two models introduced in DeepMind’s research papers. GraphCast is designed to perform high-resolution atmospheric simulations using the ERA5 dataset from ECMWF, while GenCast extends the approach with diffusion-based ensemble forecasting for probabilistic weather prediction. ... -
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TimeMixer
Decomposable Multiscale Mixing for Time Series Forecasting
...The architecture introduces specialized components such as Past-Decomposable-Mixing blocks, which extract information from historical sequences at different scales, and Future-Multipredictor-Mixing modules that combine predictions from multiple forecasting paths. This design allows the model to integrate complementary information across scales and produce more accurate predictions for complex temporal patterns. -
Point of Sale. Powerful and Simple.Vibe Retail is an all-in-one retail point-of-sale and operations platform built for single-store and multi-location retailers seeking to unify inventory, sales, staff and customer data from one mobile-friendly interface. The system lets you track inventory across locations and warehouses, handle item variations (size, color, material), manage purchase orders and supplier deliveries, print custom barcodes, and transfer stock between stores in real time. On the sales side, Vibe supports multiple payment types (cards, cash, checks, gift cards, EBT), layaway workflows, serial number tracking, delivery management, loyalty programs and branded receipts. Retailers can integrate with online platforms (such as Shopify and WooCommerce), sync in-store and online sales, access 40+ real-time reports on sales, inventory and performance, set up promotions and discounts, and print receipts from mobile devices.
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DSGE.jl
Solve and estimate Dynamic Stochastic General Equilibrium models
DSGE.jl is a Julia package developed by the Federal Reserve Bank of New York for estimating and analyzing dynamic stochastic general equilibrium (DSGE) models. It provides tools for Bayesian estimation, filtering, forecasting, and model comparison, supporting both academic research and policy applications. DSGE.jl includes pre-configured models used by central banks and offers extensibility for custom macroeconomic modeling. -
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Darts
A python library for easy manipulation and forecasting of time series
darts is a Python library for easy manipulation and forecasting of time series. It contains a variety of models, from classics such as ARIMA to deep neural networks. The models can all be used in the same way, using fit() and predict() functions, similar to scikit-learn. The library also makes it easy to backtest models, combine the predictions of several models, and take external data into account. -
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sktime
A unified framework for machine learning with time series
...It provides a unified interface for multiple time series learning tasks. Currently, this includes time series classification, regression, clustering, annotation, and forecasting. It comes with time series algorithms and scikit-learn compatible tools to build, tune and validate time series models. Our objective is to enhance the interoperability and usability of the time series analysis ecosystem in its entirety. sktime provides a unified interface for distinct but related time series learning tasks. It features dedicated time series algorithms and tools for composite model building such as pipelining, ensembling, tuning, and reduction, empowering users to apply an algorithm designed for one task to another. -
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Surya
Implementation of the Surya Foundation Model for Heliophysics
...It is designed to forecast solar phenomena—such as flares, solar wind, irradiance, and active region behavior—by predicting future solar images with a sophisticated long–short vision transformer architecture, thereby enabling improved space weather forecasting. Foresees solar flares, wind, EUV spectra, and active region formation in advance. Achieves approximately 16% improvement in forecasting accuracy over traditional methods. 366-million‑parameter foundation model capturing general-purpose solar representations. -
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Kronos
A Foundation Model for the Language of Financial Markets
...The system introduces a novel tokenization approach that converts continuous financial data into discrete tokens, enabling the model to process market behavior similarly to language. This allows Kronos to perform a variety of quantitative tasks such as forecasting, pattern recognition, and anomaly detection within financial datasets. It is optimized for the noisy and complex nature of market data, distinguishing it from general-purpose time-series models. The project includes multiple pre-trained model sizes and tools for fine-tuning, making it adaptable to different computational constraints and use cases. -
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NVIDIA Earth2Studio
Open-source deep-learning framework
NVIDIA Earth2Studio is an open-source Python package and framework designed to accelerate the development and deployment of AI-driven weather and climate science workflows. It provides a unified API that lets researchers, data scientists, and engineers build complex forecasting and analysis pipelines by combining modular prognostic and diagnostic AI models with a diverse range of real-world data sources such as global forecast systems, reanalysis datasets, and satellite feeds. The toolkit makes it easy to run deterministic and ensemble forecasts, swap models interchangeably, and process large geophysical datasets with Xarray structures, enabling experimentation with state-of-the-art deep learning models for climate and atmospheric prediction. ... -
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Plandex
AI driven development in your terminal
Plandex is an AI-powered project planning and scheduling tool that optimizes resource allocation and workflow efficiency using predictive algorithms. -
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River ML
Online machine learning in Python
River is a Python library for online machine learning. It aims to be the most user-friendly library for doing machine learning on streaming data. River is the result of a merger between creme and scikit-multiflow. -
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Nixtla TimeGPT
TimeGPT-1: production ready pre-trained Time Series Foundation Model
TimeGPT is a production ready, generative pretrained transformer for time series. It's capable of accurately predicting various domains such as retail, electricity, finance, and IoT with just a few lines of code. Whether you're a bank forecasting market trends or a startup predicting product demand, TimeGPT democratizes access to cutting-edge predictive insights, eliminating the need for a dedicated team of machine learning engineers. A generative model for time series. TimeGPT is capable of accurately predicting various domains such as retail, electricity, finance, and IoT. -
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Random Cut Forest by AWS
An implementation of the Random Cut Forest data structure
...RCFs were originally developed at Amazon to use in a nonparametric anomaly detection algorithm for streaming data. Later new algorithms based on RCFs were developed for density estimation, imputation, and forecasting. The different directories correspond to equivalent implementations in different languages, and bindings to to those base implementations, using language-specific features for greater flexibility of use. -
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Transformers in Time Series
A professionally curated list of awesome resources
...The project was created to systematically organize the rapidly growing research field that applies transformer architectures to time series modeling tasks. It compiles literature from major conferences and journals and categorizes them by application domains such as forecasting, anomaly detection, and classification. The repository also provides a taxonomy that helps researchers understand different architectural variations of transformers designed for time series data. These models are particularly important because transformers can capture long-range dependencies in sequential data, which makes them well suited for complex temporal patterns in real-world datasets. -
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Stock prediction deep neural learning
Predicting stock prices using a TensorFlow LSTM
Predicting stock prices can be a challenging task as it often does not follow any specific pattern. However, deep neural learning can be used to identify patterns through machine learning. One of the most effective techniques for series forecasting is using LSTM (long short-term memory) networks, which are a type of recurrent neural network (RNN) capable of remembering information over a long period of time. This makes them extremely useful for predicting stock prices. Predicting stock prices is a complex task, as it is influenced by various factors such as market trends, political events, and economic indicators. ... -
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OpenOps
The batteries-included, No-Code FinOps automation platform
...At its core, OpenOps provides a visual workflow builder that lets teams construct automated processes for cloud cost optimization, budgeting, tagging, allocation, forecasting, and anomaly management without writing code, making complex financial workflows approachable for both technical and financial users. The platform includes an integrated spreadsheet-like database called OpenOps Tables and built-in analytics for tracking metrics and visualizing cost trends, enabling teams to consolidate disparate cloud cost data into actionable dashboards. ... -
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Prophet
Tool for producing high quality forecasts for time series data
Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well. -
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Granite TSFM
Foundation Models for Time Series
granite-tsfm collects public notebooks, utilities, and serving components for IBM’s Time Series Foundation Models (TSFM), giving practitioners a practical path from data prep to inference for forecasting and anomaly-detection use cases. The repository focuses on end-to-end workflows: loading data, building datasets, fine-tuning forecasters, running evaluations, and serving models. It documents the currently supported Python versions and points users to where the core TSFM models are hosted and how to wire up service components. ... -
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PostgresML
The GPU-powered AI application database
...Leverage multiple types of natural language processing and machine learning models such as vector search and personalization with embeddings to improve search results. Leverage your data with time series forecasting to garner key business insights. Build statistical and predictive models with the full power of SQL and dozens of regression algorithms. Return results and detect fraud faster with ML at the database layer. PostgresML abstracts the data management overhead from the ML/AI lifecycle by enabling users to run ML/LLM models directly on a Postgres database.
