StarTime uses a hierarchical temporal tree to enable sparse or aggregated coefficient selection in high-order autoregressions and mixed-frequency regressions, with new error bounds and simulation improvements over benchmarks.
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Kling-Gupta linear regression scales the OLS coefficient vector by a variance-inflation factor based on sample moments, preserves response variance on the training set, and converges almost surely to explicit population limits while maximizing KGE but not NSE.
Matrix-HAR model with multi-horizon lags and renewable generation inputs improves one-week forecasts of realized covariation and spread risk premia versus standard backward-looking volatility methods in electricity markets.
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Sparse Tree-Based Aggregation for Time Series Regressions
StarTime uses a hierarchical temporal tree to enable sparse or aggregated coefficient selection in high-order autoregressions and mixed-frequency regressions, with new error bounds and simulation improvements over benchmarks.