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

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