# DMS, AE, DAA: methods and applications of adaptive time series model selection, ensemble, and financial evaluation

arXiv: https://arxiv.org/abs/2110.11156 · HTML: https://mlsanity.com/paper/2110.11156/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 25 references that are in the map.
- First posted on arXiv 21 Oct 2021.

## Builds on (most cited first, 6 of 6)

- [Universal features of price formation in financial markets: perspectives from Deep Learning](https://mlsanity.com/paper/1803.06917/) (2018)
- [Narrowest-Over-Threshold Detection of Multiple Change-points and Change-point-like Features](https://mlsanity.com/paper/1609.00293/) (2016)
- [Visual Time Series Forecasting: An Image-driven Approach](https://mlsanity.com/paper/2011.09052/) (2020)
- [Changepoint Detection in the Presence of Outliers](https://mlsanity.com/paper/1609.07363/) (2016)
- [A Linear Transportation Lp Distance for Pattern Recognition](https://mlsanity.com/paper/2009.11262/) (2020)
- [Time-Varying Parameters as Ridge Regressions](https://mlsanity.com/paper/2009.00401/) (2020)

## Abstract (arXiv)

We introduce three adaptive time series learning methods, called Dynamic Model Selection (DMS), Adaptive Ensemble (AE), and Dynamic Asset Allocation (DAA). The methods respectively handle model selection, ensembling, and contextual evaluation in financial time series. Empirically, we use the methods to forecast the returns of four key indices in the US market, incorporating information from the VIX and Yield curves. We present financial applications of the learning results, including fully-automated portfolios and dynamic hedging strategies. The strategies strongly outperform long-only benchmarks over our testing period, spanning from Q4 2015 to the end of 2021. The key outputs of the learning methods are interpreted during the 2020 market crash.