NeuralProphet: A simple forecasting package

[AAAI-23 Oral] Official implementation of the paper "Are Transformers Effective for Time Series Forecasting?"

Programs for stock prediction and evaluation

Web app to predict closing stock prices in real time using Facebook's Prophet time series algorithm with a multi-variate, single-step time series forecasting strategy.

PyTorch code for CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting (ICLR 2022)

About Code release for "FECAM: Frequency Enhanced Channel Attention Mechanism for Time Series Forecasting" ⌚

Detection and Prediction of Users Attitude Based on Real-Time and Batch Sentiment Analysis of Facebook Comments

This MVP data web app uses the Streamlit framework and Facebook's Prophet forecasting package to generate a dynamic forecast from your own data.

COVID-19 infectious forecasting using SEIR model and R0 estimation

An adaptive model for prediction of one day ahead foreign currency exchange rates using machine learning algorithms

Time series forecast using deep learning transformers (simple, XL, compressive). Implementation in Pytorch and Pytorch Lightning.

🏘️ The Town Energy Balance (TEB) model software and platform

Code for paper "Sparse Variational Gaussian Process based Day-ahead Probabilistic Wind Power Forecasting", IEEE Transactions on Sustainable Energy

COVID-19 spread shiny dashboard with a forecasting model, countries' trajectories graphs, and cluster analysis tools

Forecast of the level of pollution in the next hour in Beijing based on historical information

Cash Flow Forecasting Challenge held in 2020 on the popular TopCoder hackathon platform. I created my ML forecasting model using Python SciPy libraries. The step-by-step guide from data exploration to analysis has been shown in the notebook.

Novel algorithms to predict Remaining Useful Life (RUL) on NASA’s benchmark dataset, CMAPSS turbofan engine degradation simulation.

This repository will work around solving the problem of food demand forecasting using machine learning.

Predictive Analytics

Analytics Vidhya Hackathon

LSTM forecaster showcasing stateful LSTM

Forecasting sales and economic demand for businesses with a time series approach using NeuralProphet

Solar Irradiance Forecasting Using Deep Learning Techniques

Using the Prophet package published by Facebook to do time series forecasting. This is a beginner's level walk-through

Prediction of material microstructure evolution via convolutional LSTM neural networks. Implementation in pytorch.

Enhanced N-BEATS for Mid-Term Electricity Demand Forecasting

The purpose of this study is to predict the strength and locations of earthquakes in Indonesia in the coming years and to determine the maximum earthquake strength that will occur in 2024 using Prophet Forecasting model.

Python scripts from CryosphereComputing

The aim of this code is to show the preliminary results of the forecast for the term structure (with different maturities) of the Mexican government bonds using different types of models.

Energy Sector | Time Series