Judgyprophet
Forecasting for knowable future events using Bayesian informative priors (forecasting with judgmental-adjustment).
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Orbit | 1,776 |  | 1 | 3 months ago | 21 | January 29, 2023 | 54 | other | Python | |
| A Python package for Bayesian forecasting with object-oriented design and probabilistic models under the hood. | ||||||||||
| Pecan | 193 | 3 months ago | 452 | other | R | |||||
| The Predictive Ecosystem Analyzer (PEcAn) is an integrated ecological bioinformatics toolbox. | ||||||||||
| Judgyprophet | 51 | 2 years ago | 2 | March 23, 2022 | apache-2.0 | Python | ||||
| Forecasting for knowable future events using Bayesian informative priors (forecasting with judgmental-adjustment). | ||||||||||
| Bayesmodels | 46 | 2 years ago | 2 | June 28, 2021 | 6 | other | R | |||
| The Tidymodels Extension for Bayesian Models | ||||||||||
| Demest | 21 | 6 months ago | 13 | other | R | |||||
| Bayesian demographic estimation and forecasting. | ||||||||||
| Mf Bavart | 20 | 3 months ago | apache-2.0 | R | ||||||
| MF-BAVART model introduced in "Nowcasting in a Pandemic using Non-Parametric Mixed Frequency VARs" | ||||||||||
| Predmarket | 10 | 4 years ago | 1 | HTML | ||||||
| Agent-based computational simulation analysis of trading behavior in climate change prediction markets | ||||||||||
| Election_pycast | 6 | 7 years ago | mit | Jupyter Notebook | ||||||
| PyMC3 implementation of Drew Linzer’s dynamic Bayesian election forecasting model | ||||||||||
| N Of 1 Ml | 5 | 6 years ago | Jupyter Notebook | |||||||
| Bitpredict | 5 | 6 years ago | R | |||||||
| This project is concerned with predicting the price of Bitcoin using machine learning. The goal is to ascertain with what accuracy can the direction of Bitcoin price in USD can be predicted. The task is achieved with varying degrees of success through the implementation of Bayesian Regression.The popular ARIMA model for time series forecasting is implemented as a comparison to Holt’s Forecasting Model, exponential triple smoothing and Bayesian Regression. It can clearly be seen that Bayesian regression gives the best forecasting model for bitcoin price prediction. It has a very low Mean accuracy prediction error of 7.82 which is way lower compared to other models. The model is run only for 10 iterations or Markov chains and increasing this number would further improve the model accuracy. The implementation is done in R. | ||||||||||
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