Out-Of-Sample Time Series Forecasting: OOS introduces a comprehensive framework for time series forecasting with traditional econometric and modern machine learning techniques.

Forecast uncertainty based on model averaging

This code mainly computes the forecast of headline inflation using different aproaches. Likewise presents the forecast evaluation for each model along different points in a span period.

This repository contains the R-Package for a novel time series forecasting method designed to handle very large sets of predictive signals, many of which may be irrelevant or have only short-lived predictive power.

Honours research project for Sapphire Li (2023)

End-to-End Python implementation of Liu & Cheng's (2026) methodology for U.S. Treasury yield curve forecasting. Combines Factor-Augmented Dynamic Nelson-Siegel models, High-Dimensional Random Forests, and Distributionally Robust Optimization (DRO) for risk-aware ensemble forecasting under ambiguity.

📈 Forecast U.S. Treasury yield curves with a robust machine learning approach, enhancing accuracy and decision-making in finance.