NeuralProphet: A simple forecasting package
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Updated
Jun 20, 2024 - Python
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.
Detection and Prediction of Users Attitude Based on Real-Time and Batch Sentiment Analysis of Facebook Comments
PyTorch code for CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting (ICLR 2022)
This MVP data web app uses the Streamlit framework and Facebook's Prophet forecasting package to generate a dynamic forecast from your own data.
An adaptive model for prediction of one day ahead foreign currency exchange rates using machine learning algorithms
COVID-19 infectious forecasting using SEIR model and R0 estimation
About Code release for "FECAM: Frequency Enhanced Channel Attention Mechanism for Time Series Forecasting" ⌚
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.
MITx - MicroMasters Program on Statistics and Data Science - Data Analysis: Statistical Modeling and Computation in Applications - Fourth Project
🏘️ The Town Energy Balance (TEB) model software and platform
🌀 🏘️ The WRF-TEB software repository
Code for paper "Sparse Variational Gaussian Process based Day-ahead Probabilistic Wind Power Forecasting", IEEE Transactions on Sustainable Energy
Predictive Analytics
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.
Time series analysis is performed on the Berkeley Earth Surface Temperature dataset.
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