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1 Markets I: The Ecosystem and Exchange-Traded Marketsالأسواق عبر الإنترنت 2 Markets II: Rates, FX and Creditالأسواق عبر الإنترنت 3 Markets III: Commodities, Energy and Cryptoالأسواق عبر الإنترنت 4 Quantitative Methodsالأساليب عبر الإنترنت 5 Derivatives and Volatilityالمشتقات عبر الإنترنت 6 Rates, Credit, XVA and Riskالفائدة والائتمان والمخاطر عبر الإنترنت 7 Research Craft: Predictors, Backtests, Measurement, Portfoliosالبحث عبر الإنترنت 8 Strategies I: Equities and Futuresالاستراتيجيات عبر الإنترنت 9 Strategies II: Volatility, Relative Value, Macro and the Bank Desksالاستراتيجيات عبر الإنترنت 10 Microstructure and Executionالتنفيذ عبر الإنترنت 11 Market Making and High-Frequency Tradingصناعة السوق عبر الإنترنت 12 Machine Learning for Marketsتعلم الآلة عبر الإنترنت 13 Low-Latency Softwareالتكنولوجيا عبر الإنترنت 14 Networks, Hardware and Trading Infrastructureالتكنولوجيا عبر الإنترنت 15 Research, Data and Risk Platformsالتكنولوجيا عبر الإنترنت 16 The Desk and the Firmالشركة عبر الإنترنت 17 The Industry: Firms, Roles and Careersالمسارات المهنية عبر الإنترنت 18 The Interview Bookالمسارات المهنية عبر الإنترنت
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Quantitative Finance · المسرد

ما معنى Cross-validation؟

Definition 16.7 Quantitative Methods · الفصل 16 — Linear Models under Stress

Cross-validation chooses a penalty by fitting on part of the data and measuring the prediction error on the rest, over several splits. With time series, every training set must precede its test set, with a gap (a purge) at least as long as the dependence between the targets, so that no information from the test period leaks into the fit.

Lasso path of the thirty standardised predictors as the penalty falls from 1 to 10-4: the five true signals in blue, the twenty-five noise predictors in grey, and the cross-validated penalty 0.040 (dashed), where five coefficients are nonzero. Data: the chapter’s tutorial, seeded.
Figure 16.2. Lasso path of the thirty standardised predictors as the penalty falls from 1 to 10−410^{-4}: the five true signals in blue, the twenty-five noise predictors in grey, and the cross-validated penalty 0.040 (dashed), where five coefficients are nonzero. Data: the chapter’s tutorial, seeded.
Time-ordered cross-validation error of the three penalised regressions against the penalty (five expanding folds with a five-day purge, on the first 1 500 days). Each curve has an interior minimum: too little penalty fits noise, too much discards signal. Data: the chapter’s tutorial, seeded.
Figure 16.3. Time-ordered cross-validation error of the three penalised regressions against the penalty (five expanding folds with a five-day purge, on the first 1 500 days). Each curve has an interior minimum: too little penalty fits noise, too much discards signal. Data: the chapter’s tutorial, seeded.
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