🌧️ Forecast the future to prepare for rainy days
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Updated
May 29, 2024 - TypeScript
🌧️ Forecast the future to prepare for rainy days
This repository contains the source code and additional resources for the paper "Leveraging Physics-Informed Neural Networks as Solar Wind Forecasting Models". The paper discusses the challenges of solar wind forecasting and the application of Physics-Informed Neural Networks (PiNNs) to improve prediction accuracy and computational efficiency.
This repository contains code for the final project for the course "Data Science, Prediction, and Forecasting" at Aarhus University in Denmark, a course at a degree in MSc of Cognitive Science.
PyTorch implementation of Ryan Keisler's 2022 "Forecasting Global Weather with Graph Neural Networks" paper (https://arxiv.org/abs/2202.07575)
A bitcoin and stock predictor developed to inform of any potential investment opportunities or downfalls.
An open source library for Fuzzy Time Series in Python
Recurrent neural networks to predict solar radiation measurements.
A Time Series Analysis of the healthyverse R pacakges
Provide exploratory data analysis of the water level dataset from the Three Gorges dam in China as well as develop a machine learning model to forecast upstream water levels.
Forecasting the quality of cloud services using LSTM networks
Streamlit app to train, evaluate and optimize a Prophet forecasting model.
A Time series Data modelling to forecast Gambling Addiction Signs in Players using K-Means Clustering, ARIMA/SARIMA and LSTM to forecast wagering patterns
Code Repository
automatic driving simulator development in python, requirements coppelia, matlab
gui suite for biomedic profesional (dentist) coded on python
AI4EF (AI for energy efficiency) is a machine learning based software that assists the renovation procedure of buildings alongside the installation of solar panels.
Time series methods for public transit ridership forecasting
Modelo de Pronostico Meteorologico realizado con Aprendizaje Supervisado para realizar un pronostico de precipitacion a 6 horas en la Ciudad de Buenos Aires, en base a los datos suministrados por una API
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