# Dynamic Bayesian Predictive Synthesis in Time Series Forecasting

arXiv: https://arxiv.org/abs/1601.07463 · HTML: https://mlsanity.com/paper/1601.07463/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jan 2016.

## Built on by (most cited first, 7 of 7)

- [Forecast combinations: an over 50-year review](https://mlsanity.com/paper/2205.04216/) (2022)
- [Learning-to-Defer in Non-Stationary Time Series via Switching State-Space Models](https://mlsanity.com/paper/2601.22538/) (2026)
- [Causal Q-Aggregation for CATE Model Selection](https://mlsanity.com/paper/2310.16945/) (2023)
- [Bayesian Spatial Predictive Synthesis](https://mlsanity.com/paper/2203.05197/) (2022)
- [Ensemble Prediction via Covariate-dependent Stacking](https://mlsanity.com/paper/2408.09755/) (2024)
- [Bayesian Portfolio Optimization by Predictive Synthesis](https://mlsanity.com/paper/2510.07180/) (2025)
- [Predictive Density Combination Using a Tree-Based Synthesis Function](https://mlsanity.com/paper/2311.12671/) (2023)

## Abstract (arXiv)

We discuss model and forecast combination in time series forecasting. A foundational Bayesian perspective based on agent opinion analysis theory defines a new framework for density forecast combination, and encompasses several existing forecast pooling methods. We develop a novel class of dynamic latent factor models for time series forecast synthesis; simulation-based computation enables implementation. These models can dynamically adapt to time-varying biases, miscalibration and inter-dependencies among multiple models or forecasters. A macroeconomic forecasting study highlights the dynamic relationships among synthesized forecast densities, as well as the potential for improved forecast accuracy at multiple horizons.