forecasting-model

NeuralProphet: A simple forecasting package

This is the official implementation for AAAI-23 Oral 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)

COVID-19 infectious forecasting using SEIR model and R0 estimation

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.

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

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

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

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

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

Predictive Analytics

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

Analytics Vidhya Hackathon

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

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.

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.