arXiv 4 Aug 2021 · Econometrics · publishedInternational Journal of Forecasting (2022) · 24 citations (OpenAlex)
arXiv:2108.02082 · PDF · DOI · OpenAlex · Extracted main text
In this work, we propose a novel framework for density forecast combination by constructing time-varying weights based on time series features, which is called Feature-based Bayesian Forecasting Model Averaging (FEBAMA). Our framework estimates weights in the forecast combination via Bayesian log predictive scores, in which the optimal forecasting combination is determined by time series features from historical information. In particular, we use an automatic Bayesian variable selection method to add weight to the importance of different features. To this end, our approach has better interpretability compared to other black-box forecasting combination schemes. We apply our framework to stock market data and M3 competition data. Based on our structure, a simple maximum-a-posteriori scheme outperforms benchmark methods, and Bayesian variable selection can further enhance the accuracy for both point and density forecasts.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Geweke, John, Amisano, Gianni (2011) Optimal Prediction Pools | 1.000 | 18 | 5 | 100% |
| 2 | Kapetanios, G, Mitchell, James, Price, Simon, Fawcett, Nicholas (2015) Generalised density forecast combinations | 1.000 | 11 | 4 | 100% |
| 3 | Monteromanso, Pablo, Athanasopoulos, George, Hyndman, Rob J, Talagal… (2020) FFORMA: Feature-based forecast model averaging | 1.000 | 7 | 5 | 100% |
| 4 | Talagala, Thiyanga S, Li, Feng, Kang, Yanfei (2022) FFORMPP: Feature-based forecast model performance prediction self | 0.928 | 4 | 4 | 100% |
| 5 | Hyndman, Rob, Kang, Yanfei, Montero-Manso, Pablo, Talagala, Thiyanga… (2020) tsfeatures: Time Series feature extraction self | 0.843 | 3 | 3 | 100% |
| 6 | Kang, Yanfei, Hyndman, Rob J, Li, Feng (2020) GRATIS: GeneRAting TIme Series with diverse and controllable characteristics self | 0.843 | 3 | 3 | 100% |
| 7 | Kononenko, Igor (1994) Estimating attributes: Analysis and extensions of RELIEF | 0.843 | 3 | 3 | 100% |
| 8 | Del Negro, Marco, Hasegawa, Raiden B, Schorfheide, Frank (2016) Dynamic prediction pools: An investigation of financial frictions and forecasting performance | 0.737 | 3 | 2 | 100% |
| 9 | Hall, Stephen G, Mitchell, James (2007) Combining density forecasts | 0.644 | 4 | 1 | 100% |
| 10 | Harvey, David, Leybourne, Stephen, Newbold, Paul (1997) Testing the equality of prediction mean squared errors | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 67 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Feature-based intermittent demand forecast combinations: accuracy and inventory implications | 0.405 | 1 | 1 |
| 2 | Predictive Density Combination Using a Tree-Based Synthesis Function | 0.405 | 1 | 1 |