सभी किताबें

1 Primary & Middle School Mathematicsकक्षा 1–9 ऑनलाइन 2 High School Mathematicsकक्षा 10–12 ऑनलाइन 3 University Mathematics — Year 1स्नातक वर्ष 1 ऑनलाइन 4 University Mathematics — Year 2स्नातक वर्ष 2 ऑनलाइन 5 University Mathematics — Year 3स्नातक वर्ष 3 ऑनलाइन

1 Primary & Middle School Physicsकक्षा 1–9 ऑनलाइन 2 High School Physicsकक्षा 10–12 ऑनलाइन 3 University Physics — Year 1स्नातक वर्ष 1 ऑनलाइन 4 University Physics — Year 2स्नातक वर्ष 2 ऑनलाइन 5 University Physics — Year 3स्नातक वर्ष 3 ऑनलाइन

1 Primary & Middle School Biologyकक्षा 1–9 ऑनलाइन 2 High School Biologyकक्षा 10–12 ऑनलाइन 3 University Biology — Year 1स्नातक वर्ष 1 ऑनलाइन 4 University Biology — Year 2स्नातक वर्ष 2 ऑनलाइन 5 University Biology — Year 3स्नातक वर्ष 3 ऑनलाइन

1 Markets I: The Ecosystem and Exchange-Traded Marketsबाज़ार ऑनलाइन 2 Markets II: Rates, FX and Creditबाज़ार ऑनलाइन 3 Markets III: Commodities, Energy and Cryptoबाज़ार ऑनलाइन 4 Quantitative Methodsविधियाँ ऑनलाइन 5 Derivatives and Volatilityडेरिवेटिव्स ऑनलाइन 6 Rates, Credit, XVA and Riskब्याज दरें, क्रेडिट और जोखिम ऑनलाइन 7 Research Craft: Predictors, Backtests, Measurement, Portfoliosअनुसंधान ऑनलाइन 8 Strategies I: Equities and Futuresरणनीतियाँ ऑनलाइन 9 Strategies II: Volatility, Relative Value, Macro and the Bank Desksरणनीतियाँ ऑनलाइन 10 Microstructure and Executionनिष्पादन ऑनलाइन 11 Market Making and High-Frequency Tradingमार्केट मेकिंग ऑनलाइन 12 Machine Learning for Marketsमशीन लर्निंग ऑनलाइन 13 Low-Latency Softwareतकनीक ऑनलाइन 14 Networks, Hardware and Trading Infrastructureतकनीक ऑनलाइन 15 Research, Data and Risk Platformsतकनीक ऑनलाइन 16 The Desk and the Firmफ़र्म ऑनलाइन 17 The Industry: Firms, Roles and Careersकरियर ऑनलाइन 18 The Interview Bookकरियर ऑनलाइन

सभी गेम Math Fuse Word Reactor Atlas Dash

ऐप्स परिचय

Coach

English Français Español Nederlands Português हिन्दी العربية Bahasa Indonesia

लॉग इन पढ़ना शुरू करें

Quantitative Finance · शब्दावली

Conformal prediction क्या है?

Definition 11.6 Machine Learning for Markets · अध्याय 11 — Probabilistic Models and Uncertainty

Conformal prediction turns any forecast into prediction sets with a coverage guarantee: a score $s_i = s(x_i, y_i)$ (for example $|y_i - \hat y_i|$ , or that divided by a predicted standard deviation) is computed on $n$ calibration examples, and the set for a new $x$ is $\{y: s(x, y)\le\hat q\}$ with $\hat q$ the $\lceil(n+1)(1 - \alpha)\rceil$ -th smallest calibration score (split conformal; Vovk, Gammerman and Shafer, 2005). The adaptive version updates the level online, $\alpha_{t+1} = \alpha_t + \gamma(\alpha - \mathrm{err}_t)$ , to keep the long-run error rate at $\alpha$ when the data drift (Gibbs and Candès, 2021).

Coverage of 90% intervals on the test days, before and after the volatility regime (dashed: the nominal 90%). The three models’ own intervals, and conformal intervals around the boosted mean forecast: split conformal with raw errors, with errors normalised by the ensemble’s standard deviation, and adaptive. Data: ml_uncert.scores and ml_uncert.conformal. — Figure 11.1. Coverage of 90% intervals on the test days, before and after the volatility regime (dashed: the nominal 90%). The three models’ own intervals, and conformal intervals around the boosted mean forecast: split conformal with raw errors, with errors normalised by the ensemble’s standard deviation, and adaptive. Data: ml_uncert.scores and ml_uncert.conformal.

One asset through the regime change at day 2 100 (vertical line): realised next-day returns, the ensemble’s 90% interval, and the adaptive conformal interval around the boosted mean. Both widen after the break; the conformal band widens further as its errors accumulate. Data: ml_uncert.conformal. — Figure 11.2. One asset through the regime change at day 2 100 (vertical line): realised next-day returns, the ensemble’s 90% interval, and the adaptive conformal interval around the boosted mean. Both widen after the break; the conformal band widens further as its errors accumulate. Data: ml_uncert.conformal.

अध्याय में पढ़ें →