Search Results for "forecasting" - Page 2
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Showing 84 open source projects for "forecasting"
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Secure File Transfer for Windows with Cerberus by RedwoodCerberus supports unlimited users and connections on a single IP, with built-in encryption, 2FA, and a browser-based web client — all deployable in under 15 minutes with a 25-day free trial.
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Kaggle Solutions
Collection of Kaggle Solutions and Ideas
...The repository also highlights important machine learning concepts such as feature engineering, cross-validation strategies, ensemble modeling, and post-processing methods commonly used in winning solutions. Because the content is organized by competition categories such as computer vision, natural language processing, tabular data, and time-series forecasting, users can explore techniques relevant to specific problem types. -
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MiroThinker
MiroThinker is an open source deep research agent
...Rather than simply generating responses from a single prompt, the agent performs structured multi-step reasoning processes that involve searching for information, analyzing evidence, and synthesizing conclusions. The platform is optimized for research tasks such as financial forecasting, knowledge discovery, and large-scale information synthesis. MiroThinker has been evaluated on several agent benchmarks and has demonstrated strong performance on tests designed to measure deep research capabilities. -
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PyTorch Geometric Temporal
Spatiotemporal Signal Processing with Neural Machine Learning Models
...Moreover, it comes with an easy-to-use dataset loader, train-test splitter and temporal snaphot iterator for dynamic and temporal graphs. The framework naturally provides GPU support. It also comes with a number of benchmark datasets from the epidemiological forecasting, sharing economy, energy production and web traffic management domains. Finally, you can also create your own datasets. The package interfaces well with Pytorch Lightning which allows training on CPUs, single and multiple GPUs out-of-the-box. PyTorch Geometric Temporal makes implementing Dynamic and Temporal Graph Neural Networks quite easy - see the accompanying tutorial. ... -
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Zero to Mastery Deep Learning TensorFlow
All course materials for the Zero to Mastery Deep Learning with TF
...It is structured as a series of progressively complex Jupyter notebooks that emphasize writing and understanding code before diving into theory, reinforcing learning through repetition and application. The material covers core machine learning workflows including regression, classification, computer vision, natural language processing, and time series forecasting, allowing users to build a well-rounded understanding of modern AI tasks. It also integrates milestone projects that simulate real-world scenarios, helping users translate abstract concepts into deployable solutions. -
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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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applied-ml
Papers & tech blogs by companies sharing their work on data science
The applied-ml repository is a rich, curated collection of papers, technical articles, and case-study blog posts about how machine learning (ML) and data-driven systems are applied in real production environments by major companies. Instead of focusing solely on theoretical ML research, this repo highlights industry-scale challenges: data collection, quality, infrastructure, feature stores, model serving, monitoring, scalability, and how ML is embedded in product workflows. It acts as a... -
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BoolFund
Funds have risks, and investments must be made cautiously.
A powerful fund analysis and management system software. It integrates multiple functions such as fund information, fund market data, fund analysis, fund management, and fund forecasting, maximizing the solution to problems investors face in choosing funds and managing returns. -
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BRAMS-FURNAS
Weather Forescast Model with chemistry, aerosols and transport .
BRAMS IS A NUMERICAL MODELING SYSTEM DESIGNED FOR REGIONAL SCALE ATMOSPHERIC FORECASTING AND RESEARCH, WITH FOCUS ON ATMOSPHERIC CHEMISTRY, AIR QUALITY AND BIOGEOCHEMICAL CYCLES. -
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Django-CRM - Free Open Source Software
Enterprise CRM software + Tasks management, Email marketing and more!
Django CRM system is easy to use and can be run on a personal laptop for a private entrepreneur (for example) or on a cloud web server for a large enterprise. Written in Python, CRM is an open-source software package for managing customer interactions and sales. This free CRM software is a powerful tool designed to optimize workflows, and support data-driven decisions. Key features include role-based access control, intuitive navigation, powerful filtering, and search functionalities. The... -
Our Free Plans just got better! | Auth0You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
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Monitoring Storage, SAN, LAN
Storage, SAN, LAN Performance Monitoring: IBM,NetApp,Hitachi,HPE,EMC
The tool offers you end-to-end views of your storage environment including LAN and SAN and can save you significant money in operation monitoring and by predicting utilization bottlenecks in your virtualized environment. You can also generate policy-based alerts, view overall health status of your systems, reduce service downtime, use capacity and forecasting data - Real-time storage performance visibility in LAN and SAN multi-vendor environment - Historical reporting (graph, CSV, PDF) - Alerting based on performance thresholds - Storage event monitoring The tool supports enterprise class level storage devices from major storage vendors like: IBM, Dell EMC, NetAPP, HPE, Hitachi, Lenovo, Pure Storage, Huawei, Dot Hill, INFINIDAT, Fujitsu, DataCore, Quantum, QNAP, FalconStor,Ceph, Synology, RAIDIX, Qumulo,Inspur,Veritas Monitoring of the SAN supports Brocade, Qlogic and Cisco SAN switches Monitoring of the LAN network Try demo at http://demo.stor2rrd.com -
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Data Envelopment Analysis using Stata
Develop a user written Data Envelopment Analysis package in Stata.
The goal of this project is to develop a Data Envelopment Analysis(DEA) program using Stata programming language. This is the replacement of "deastata" project that we maintained for the purpose of version management. We call the program package "DEAS" which stands for Data Envelopment Analysis using Stata. DEAS covers the basic models of DEA and extensions including CCR, BCC, SBM, Super-efficiency Model, Allocative Model(Profit, Revenue, Cost), (Global) Malmquist Productivity Index... -
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Premium Markets
Premium Markets is an automated financial technical analysis system.
Premium Markets is an automated financial technical analysis system. With Premium Markets you can edit and create your own indicators as a composition of more than a hundred common technical analysis functions. It implements a graphical environment for monitoring financial technical analysis of the main stocks and shares markets and currencies. The standalone application includes a full integration of ta-lib C/C++ libraries. Be aware that the provided App available for download may... -
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Capacity Planning & Management Software
Free & Open Capacity Planning Software in a Complete Virtual Machine
Every Profession has some sort of Resource to manage, be it materials, money, physical or logical resources & with it some vital queries to be answered: At what Rate is my Resource getting consumed? How Long will current stock last? What will be near future requirement? In Just Two Simple steps of Defining Your Resource & Actively Tracking its Consumption, you will be in total control of your Resource, with this Software. After starting this VM, from any PC on your Local Network,... -
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A tool to support individuals and teams using high-maturity, metrics-intensive project management methodologies [such as PSP(SM) and TSP(SM)]. Supports personal and team earned value tracking and forecasting; simplifies metrics collection and analysis.
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Merlion
A Machine Learning Framework for Time Series Intelligence
...It provides an end-to-end machine learning framework that includes loading and transforming data, building and training models, post-processing model outputs, and evaluating model performance. It supports various time series learning tasks, including forecasting, anomaly detection, and change point detection for both univariate and multivariate time series. This library aims to provide engineers and researchers a one-stop solution to rapidly develop models for their specific time series needs, and benchmark them across multiple time series datasets. -
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Rabio
Rapid Business Information Organizer
Rapid Business Information Organizer is a software platform designed to organize projects and control their financial activities like budget, costs, earned value, forecast, projection etc. It was designed to take control of budget and costs using a flexible Cost Breakdown Structure (CBS) of cost centers, cost categories with the addition of tags. See more on https://www.rabio.eu -
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Auto-PyTorch
Automatic architecture search and hyperparameter optimization
...To bring the best of these two worlds together, we developed Auto-PyTorch, which jointly and robustly optimizes the network architecture and the training hyperparameters to enable fully automated deep learning (AutoDL). Auto-PyTorch is mainly developed to support tabular data (classification, regression) and time series data (forecasting). The newest features in Auto-PyTorch for tabular data are described in the paper "Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL" (see below for bibtex ref). Details about Auto-PyTorch for multi-horizontal time series forecasting tasks can be found in the paper "Efficient Automated Deep Learning for Time Series Forecasting" (also see below for bibtex ref). -
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Padasip
Python Adaptive Signal Processing
Padasip (Python Adaptive Signal Processing) is a Python library tailored for adaptive filtering and online learning applications, particularly in signal processing and time series forecasting. It includes a variety of adaptive filter algorithms such as LMS, RLS, and their variants, offering real-time adaptation to changing environments. The library is lightweight, well-documented, and ideal for research, prototyping, or teaching purposes. Padasip supports both supervised and unsupervised filtering modes and is built to be modular and extensible, making it easy to integrate into larger machine learning pipelines or control systems. -
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Deep learning time series forecasting
Deep learning PyTorch library for time series forecasting
Example image Flow Forecast (FF) is an open-source deep learning for time series forecasting framework. It provides all the latest state-of-the-art models (transformers, attention models, GRUs) and cutting-edge concepts with easy-to-understand interpretability metrics, cloud provider integration, and model serving capabilities. Flow Forecast was the first time series framework to feature support for transformer-based models and remains the only true end-to-end deep learning for time series forecasting framework. ... -
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TRANSIMS is an integrated system of travel forecasting tools for modeling regional transport systems. For more information, visit http://code.google.com/p/transims/
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WRF_ELEC
Storm electrification package for the WRF-ARW
NOTE: For WRF-ELEC with version 4.5.1, go to https://github.com/MicroTed/wrf4-elec This (older) project provides the external files need to compile electrification into the Weather Research and Forecasting (WRF) model (version 3.9.1.1 currently). It contains modules for the lightning discharge scheme, the screening layer parameterization, communications, and include code that calls these subroutines. Note that the required elliptic equation solver is a separate download from <http://sourceforge.net/projects/boxmg4wrf> -
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Forecasting Best Practices
Time Series Forecasting Best Practices & Examples
Time series forecasting is one of the most important topics in data science. Almost every business needs to predict the future in order to make better decisions and allocate resources more effectively. This repository provides examples and best practice guidelines for building forecasting solutions. The goal of this repository is to build a comprehensive set of tools and examples that leverage recent advances in forecasting algorithms to build solutions and operationalize them. ... -
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AlphaPy
Python AutoML for Trading Systems and Sports Betting
...Built on popular libraries like scikit-learn and pandas, it enables data scientists and speculators to craft predictive models, ensemble strategies, and automated forecasting systems with minimal setup. Run machine learning models using scikit-learn, Keras, xgboost, LightGBM, and CatBoost. Generate blended or stacked ensembles. Create models for analyzing the markets with MarketFlow. Develop trading systems and analyze portfolios using MarketFlow and Quantopian's pyfolio. -
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SwaNN
PSO for neural networks
SwaNN is a basic framework for neural networks based on particle swarm optimization (using the Python package PySwarms (https://pyswarms.readthedocs.io/en/latest/). The zip file contains the main programs in SwaNN.py and around 30 examples : - classification - regression - time series forecasting I need some help for class building (I am not an expert in Python nor in OOP), if somebody is interested in it... In Google Colab : https://colab.research.google.com/drive/1u6SOydDUThUrhTfaic2NiyDhh1ZGRJsH?usp=sharing What's new: - the jupyter notebook is reorganized and clean -
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fooltrader
Quant framework for stock
...Its goal is to provide a unified framework for the whole market (stock, futures, bonds, foreign exchange, digital currency, macroeconomics, etc.) for research, backtesting, forecasting, and trading. Its applicable objects include quantitative traders, teachers, and students majoring in finance, people interested in economic data, programmers, and people who like freedom and the spirit of exploration. You could write the Strategy using an event-driven or time walkway and view and analyze the performance in a uniform way.
