Forecast of energy demand in France with periodic re-training.
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Updated
Jul 2, 2024 - Python
Forecast of energy demand in France with periodic re-training.
PyTorch implementation of Ryan Keisler's 2022 "Forecasting Global Weather with Graph Neural Networks" paper (https://arxiv.org/abs/2202.07575)
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.
using project idx
Finance prediction app to view historical and current market data, weekly predictions on BTC / stocks / forex, and get support from AI driven services. Developed with Spring Boot, Maven, Thymeleaf, Django, AWS, Docker, MySQL.
Python implementation of the midasml approach
AI4EF (AI for energy efficiency) is a machine learning based software that assists the renovation procedure of buildings alongside the installation of solar panels.
Using Statistical and Machine Learning Methods to Forecast Day-Ahead Electricity Prices: The Impact and Optimal Selection of Calibration Window Lengths - Master's Thesis @ Imperial College London
A statistical library designed to fill the void in Python's time series analysis capabilities, including the equivalent of R's auto.arima function.
List of papers, code and experiments using deep learning for time series forecasting
detailed and comprehensive time-series analysis using python (includes ARIMA and SARIMA)
FastForecast is an R package that aims to provide a fast and accurate forecasting method for time series data.
Exploring forecasting for energy consumption
ADCIRC Model Repository
Implementation and comparison of ARIMA models to forecast Olist revenue for the next 14 days.
This project dives deep into customer sales data to uncover valuable insights for business decision-making. It leverages machine learning and time-series forecasting to predict customer churn, forecast product demand, and segment customers based on their purchasing behavior.
Time series analysis in the `tidyverse`
A Time Series Analysis of the healthyverse R pacakges
Comprehensive guide to time series forecasting using deep learning techniques, with practical examples and tutorials.
Build and Evaluate Drug Sales using Time Series forecasting
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