NeuralProphet: A simple forecasting package
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Updated
Jul 16, 2024 - Python
NeuralProphet: A simple forecasting package
LSTM forecaster showcasing stateful LSTM
[AAAI-23 Oral] Official implementation of the paper "Are Transformers Effective for Time Series Forecasting?"
AQI Forecaster web app using Machine Learning
This MVP data web app uses the Streamlit framework and Facebook's Prophet forecasting package to generate a dynamic forecast from your own data.
Python scripts from CryosphereComputing
Novel algorithms to predict Remaining Useful Life (RUL) on NASA’s benchmark dataset, CMAPSS turbofan engine degradation simulation.
The Forecast Brazilian Salary API is a powerful tool that allows users to predict salaries based on various factors and parameters.
Prediction of material microstructure evolution via convolutional LSTM neural networks. Implementation in pytorch.
PyTorch code for CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting (ICLR 2022)
@EcobiciMapBot Twitter bot automático cada 30min para mostrar el pronóstico de la disponibilidad en la siguiente hora para estaciones de Ecobici CDMX 🚴🏽♂️
Python code to analyse PV production and weather Data to forecast future production
Manuscript, source codes and data sets on estimating Singapore’s lower-bound SARS-CoV-2 Infection Trend In 2020.
Forecast of next month's number of car sales based on historical information
Forecast of the level of pollution in the next hour in Beijing based on historical information
Time series forecast using deep learning transformers (simple, XL, compressive). Implementation in Pytorch and Pytorch Lightning.
Regression models mapping m inputs to n outputs. I have tried to make this code as configurable as possible by using .yaml configurations. Please read the config comments and set appropriate config variables to successfully run the application. Also, documentation is generated using Sphinx 1.8.0 and can be read at 'docs_build\html\index.html' in…
An adaptive model for prediction of one day ahead foreign currency exchange rates using machine learning algorithms
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