Today, give a try to Techtonique web app, a tool designed to help you make informed, data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization. Here is a tutorial with audio, video, code, and slides: https://moudiki2.gumroad.com/l/nrhgb. 100 API requests are now (and forever) offered to every user, no matter the pricing tier.
This post was firstly submitted to the Applied Quantitative Investment Management group on LinkedIn. It illustrates a recipe implemented in Python package nnetsauce for time series forecasting uncertainty quantification (through simulation): sequential split conformal prediction + block bootstrap
Underlying algorithm:
- Split data into training set, calibration set and test set
- Obtain point forecast on calibration set
- Obtain calibrated residuals = point forecast on calibration set - true observation on calibration set
- Simulate calibrated residuals using block bootstrap
- Obtain Point forecast on test set
- Prediction = Calibrated residuals simulations + point forecast on test set
Interested in experimenting more? Here is a web app.
For more details, you can read (under review): https://www.researchgate.net/publication/379643443_Conformalized_predictive_simulations_for_univariate_time_series
Comments powered by Talkyard.