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Combinatorial purged cross-validation (CPCV) is a technique for backtesting strategies while purging and embargoing observations in a time series. CPCV improves upon classical k-fold and walk-forward cross-validation because it has an added layer of generating paths that each possesses a unique Sharpe ratio, allowing a strategy's Sharpe ratio distribution to be derived, whereas K-fold and walk forward only provided one Sharpe ratio.

My understanding is that cross-validation is a process that generates univariate dataset/time series predictions (but of course the input for fitting can be multivariate data). Given that CPCV not only groups based on folds, but also groups to produce multiple backtest paths, does this still make it a univariate predictor? If so, how can CPCV be extended to generate multivariate predictions? for example, maybe a matrix whose columns are individual prediction vectors

But even without an answer to extending it to multivariate format, I just want to understand what CPCV means for multivariate time series data, input and output, given its extra layer of paths to predict.

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