Popular GitHub Repositories for Influenza Forecasts

Time-series machine learning at scale. Built with Polars for embarrassingly parallel feature extraction and forecasts on panel data.

Stock Market Prediction Web App based on Machine Learning and Sentiment Analysis of Tweets (API keys included in code). The front end of the Web App is based on Flask and Wordpress. The App forecasts stock prices of the next seven days for any given stock under NASDAQ or NSE as input by the user. Predictions are made using three algorithms: ARIMA, LSTM, Linear Regression. The Web App combines the predicted prices of the next seven days with the sentiment analysis of tweets to give recommendation whether the price is going to rise or fall

An extension of XGBoost to probabilistic modelling

PyTorch implementation of Transformer model used in "Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case"

[NeurIPS 2023] A Dynamics-informed Diffusion Model for Spatiotemporal Forecasting

Fast, high-quality forecasts on relational and multivariate time-series data powered by new feature learning algorithms and automated ML.

Microsoft Finance Time Series Forecasting Framework (FinnTS) is a forecasting package that utilizes cutting-edge time series forecasting and parallelization on the cloud to produce accurate forecasts for financial data.

Modeling time series of electricity spot prices using Deep Learning.

Using an integrated pinball-loss objective function in various recurrent based deep learning architectures made with keras to simultaneously produce probabilistic forecasts for UK wind, solar, demand and price forecasts.

Predicting Future Influenza Virus Sequences with Machine Learning

A custom RAG-LLM model to analyse central banking forecasts

An in-development R package and a Bayesian hierarchical model jointly fitting multiple "local" wastewater data streams and "global" case count data to produce nowcasts and forecasts of both observations

Smart Farming Assistant uses advanced technology, including machine learning and CNNs, to provide farmers with crop recommendations, disease identification, weather forecasts, fertilizer recommendation, and crop management guidance through a user-friendly web app.

Data-driven COVID-19 forecasts and detection of unreported cases

An eXplainable AI system to elucidate short-term speed forecasts in traffic networks obtained by Spatio-Temporal Graph Neural Networks.

In this project, we leverage Deep Learning algorithms to build robust forecasting system that monitors the change in the demand side and aligns the supply side to make up for the inaccuracy of the forecasts and randomness in demand, helping retailers increase their inventory and planning efficiency.

Code for "Coherent Probabilistic Aggregate Queries on Long-horizon Forecasts", IJCAI 2022

Enhanced spatio-temporal electric load forecasts with less data using active deep learning

:chart_with_downwards_trend: Open-source charting library to help you visualize MindsDB forecasts :chart_with_upwards_trend:

MicroAI™ is an AI engine that can operate on low power edge and endpoint devices. It can learn the pattern of any and all time series data and can be used to detect anomalies or abnormalities, make one step ahead predictions/forecasts, and calculate the remaining life of entities (whether it is industrial machinery, small devices or the like).