Andrey davydenko (8 resultados)
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Librería: Books Puddle, New York, NY, Estados Unidos de AmericaBooks Puddle
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EUR 113,51
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Condición: New. pp. 308.
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Librería: preigu, Osnabrück, Alemaniapreigu
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EUR 73,30
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Taschenbuch. Condición: Neu. Integration of Judgmental and Statistical Approaches to Forecasting | Error metrics, visual tools, handling unaided judgment, analysis of judgmental adjustments, joint Bayesian modelling | Andrey Davydenko | Taschenbuch | Englisch | 2021 | LAP LAMBERT Academic Publishing | EAN 9786203471229 | Verantw…ortliche Person für die EU: LAP Lambert Academic Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu.
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.
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EUR 87,90
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -When it comes to forecasting, it's important to know how good your forecasting is and if there are ways to improve it. This work focuses on finding reliable and informative indicators of forecasting performance and on how to improve… forecasts with the use of judgment. Chapter 2 explores limitations of various error measures and introduces a new class of metrics (AvgRel-metrics) for measuring forecasting performance using the following rules: i) relative indicators are averaged across series using the weighted geometric mean, ii) an indicator used to evaluate forecasts must correspond to the loss function used to optimize forecasts. The AvgRelMSE and AvgRelMAE metrics are proposed to measure accuracy under quadratic and linear loss, respectively, and the AvgRelAME to measure bias. Boxplots of logs of relative indicators are used to visualize distributions. Chapters 3 and 4 look at models for handling unaided judgment & judgmental adjustments. In particular, this work introduces advanced models based on using panel data and Bayesian analysis. Chapter 5 proposes a novel approach allowing to incorporate judgment into a joint model and update forecasts as new data becomes available. 308 pp. Englisch.
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Librería: Majestic Books, Hounslow, Reino UnidoMajestic Books
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EUR 115,19
Envío por EUR 7,53Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: 4 disponibles
Condición: New. Print on Demand pp. 308.
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Librería: moluna, Greven, Alemaniamoluna
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EUR 70,05
Envío por EUR 48,99Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Davydenko AndreyAndrey has a rich experience of working as a data scientist/researcher on various projects, including credit scoring and the development of commercial software for business forecasting.…He s a Microsoft Certified Solu.
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Librería: Biblios, frankfurt am main, HESSE, AlemaniaBiblios
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EUR 114,74
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Condición: New. PRINT ON DEMAND pp. 308.
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemaniabuchversandmimpf2000
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EUR 87,90
Envío por EUR 60,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -When it comes to forecasting, it's important to know how good your forecasting is and if there are ways to improve it. This work focuses on finding reliable and informative indicators of forecasting performance and on how to improve for…ecasts with the use of judgment. Chapter 2 explores limitations of various error measures and introduces a new class of metrics (AvgRel-metrics) for measuring forecasting performance using the following rules: i) relative indicators are averaged across series using the weighted geometric mean, ii) an indicator used to evaluate forecasts must correspond to the loss function used to optimize forecasts. The AvgRelMSE and AvgRelMAE metrics are proposed to measure accuracy under quadratic and linear loss, respectively, and the AvgRelAME to measure bias. Boxplots of logs of relative indicators are used to visualize distributions. Chapters 3 and 4 look at models for handling unaided judgment & judgmental adjustments. In particular, this work introduces advanced models based on using panel data and Bayesian analysis. Chapter 5 proposes a novel approach allowing to incorporate judgment into a joint model and update forecasts as new data becomes available.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 308 pp. Englisch.
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 88,95
Envío por EUR 62,39Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - When it comes to forecasting, it's important to know how good your forecasting is and if there are ways to improve it. This work focuses on finding reliable and informative indicators of forecasting performance and on how to improve fore…casts with the use of judgment. Chapter 2 explores limitations of various error measures and introduces a new class of metrics (AvgRel-metrics) for measuring forecasting performance using the following rules: i) relative indicators are averaged across series using the weighted geometric mean, ii) an indicator used to evaluate forecasts must correspond to the loss function used to optimize forecasts. The AvgRelMSE and AvgRelMAE metrics are proposed to measure accuracy under quadratic and linear loss, respectively, and the AvgRelAME to measure bias. Boxplots of logs of relative indicators are used to visualize distributions. Chapters 3 and 4 look at models for handling unaided judgment & judgmental adjustments. In particular, this work introduces advanced models based on using panel data and Bayesian analysis. Chapter 5 proposes a novel approach allowing to incorporate judgment into a joint model and update forecasts as new data becomes available.
