davydenko andrey (8 resultados)

Condición: New. pp. 308.

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 | Verantwortliche Person für die EU: LAP Lambert Academic Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu.

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.

Condición: New. Print on Demand pp. 308.

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.

Condición: New. PRINT ON DEMAND pp. 308.

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 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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 308 pp. Englisch.

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 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.