A combined LSTM and LightGBM framework for improving deterministic and probabilistic wind energy forecasting

Runner-up team (2nd place) in AI4VN2022: Air Quality Forcasting Challenge

weatheril is an unofficial [IMS](https://ims.gov.il) (Israel Meteorological Service) python API wrapper.

[TOIS] "Privacy-Preserving Individual-Level COVID-19 Infection Prediction via Federated Graph Learning"

StockLLM: A Stock Analyzer with Comprehensive LLM Insights

Scripts to build and run WRF and WPS in docker

Multi-agent scientific analysis platform. It creates experiments, tries different methods, evaluates results, searches relevant literature, builds custom tools when needed, and generates detailed reports and presentaions.

A stock prediction application that uses Google's TimesFM (Time Series Foundation Model) to forecast stock prices from Yahoo Finance, with FastAPI serving as the backend API.

OmniEcon Nexus is an open-source, high-performance simulation engine for global micro/macro-economic analysis. Using deep learning, agent-based modeling, and optimization, it supports 5M agents for forecasting, risk analysis, policy simulation, and portfolio management. Built for governments, researchers, and developers.

A machine learning based system that predicts, analyzes, and optimizes industrial energy consumption using forecasting, anomaly detection, and digital twin simulation.

An implementation of AE LSTM based. We test our architecture on several tasks as reconstructing synthetic time series, s&p 500 stocks, and forecasting s&p 500 stocks based on the decoded information (also known as latent space) features we extract from the AE

This project involves developing and testing a trading model designed to predict stock prices and evaluate trading strategies. The core of the project includes building and training a LSTM based model for time series forecasting in addition to a RL model, evaluating its performance, and visualizing the results.

forecasting time series Singapore PSI (pm2.5) 2016-2019

The Alpha Alternator is a novel generative model designed for time-dependent data, dynamically adapting to varying noise levels in sequences.

This Model is Base On Halt & Winter Algorithm.This Model is Forecast About Seasonal Data.

AgroNomics is a machine learning web application that forecasts crop prices using historical agricultural data, seasonal trends, and region-specific variables. Built with Flask and scikit-learn, it provides nationwide coverage with state- and district-level insights, enabling farmers to make accurate, data-driven market decisions.

Streamlit platform for hotel management with AI-powered forecasting.

End-to-end sales analysis using Python, SQL, and Power BI

A small script enable easy training for XGBoost forecasting model on any time series data

Data-driven analysis and forecasting of India's onshore wind power capacity using machine learning, historical energy datasets, and renewable energy targets.