Integration of Judgmental and Statistical Approaches to Forecasting : Error metrics, visual tools, handling unaided judgment, analysis of judgmental adjustments, joint Bayesian modelling

Andrey Davydenko

ISBN 10: 6203471224 / ISBN 13: 9786203471229

Published by LAP LAMBERT Academic Publishing, 2021

About this Item

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.

Seller Inventory # 9786203471229

Bibliographic Details

• Title: Integration of Judgmental and Statistical Approaches to Forecasting : Error metrics, visual tools, handling unaided judgment, analysis of judgmental adjustments, joint Bayesian modelling
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2021
• Language: English
• ISBN 10: 6203471224
• ISBN 13: 9786203471229
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 19D millimeters 220 millimeters width by 150 millimeters height by 19 millimeters depth
• Weight: 477 grams

About this title

Synopsis

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.