Index
Advances in Business and Management Forecasting
ISBN: 978-1-78754-290-7, eISBN: 978-1-78754-289-1
ISSN: 1477-4070
Publication date: 6 September 2019
This content is currently only available as a PDF
Citation
(2019), "Index", Advances in Business and Management Forecasting (Advances in Business and Management Forecasting, Vol. 13), Emerald Publishing Limited, Leeds, pp. 135-139. https://doi.org/10.1108/S1477-407020190000013001
Publisher
:Emerald Publishing Limited
Copyright © 2019 Emerald Publishing Limited
INDEX
AdaBoost
, 94–95, 97, 99, 100, 101, 102, 103, 104
Adaptive Neuro Fuzzy Inference System (ANFIS)
, 5
Agent-based queuing model, for call center forecasting and management optimization
, 121
bisection method
, 128–131
forecasting
, 127
model construction
, 125–127
model formulation
, 122–124
input data
, 122–123
programing functions, modeling
, 123
working variables in program, modeling
, 123–124
optimization criteria
, 127–128
results
, 130–131, 132
stepwise method
, 131
ARIMA
, 4, 5
Artificial neural networks (ANNs)
, 5
Asymmetric loss function
, 55, 57–58
Bankruptcy prediction, dimension reduction in
, 83
computational results
, 86–89
evaluation metrics
, 87
experiment setup
, 86–87
model performances
, 88–89, 90, 91
dimension estimation
, 89–90, 91
dimension reduction techniques
, 84–86
Big Data
, 94
Bisection method
, 128–131
Bonobos
, 26
Boosting technique
, 84
Borderline-SMOTE
, 94
Box & Jenkins model
, 4, 5–6
Brick-and-mortar store
, 30, 31
Bureau of Economic Analysis (BEA)
, 68–69
Business cycle
, 67, 71, 72, 73, 74–75, 76–79, 81
Business forecasting
, 121, 127, 132
Buy-online-and-pick-up-in-store (BOPS)
, 25
literature review
, 28–30
basic model
, 30–37
Call center forecasting and management optimization, agent-based queuing model for
, 121
bisection method
, 128–131
forecasting
, 127
model construction
, 125–127
model formulation
, 122–124
input data
, 122–123
programing functions, modeling
, 123
working variables in program, modeling
, 123–124
optimization criteria
, 127–128
results
, 130–131, 132
stepwise method
, 131
CEO compensation, regression modeling of
peer group, at AT&T
, 115, 116, 117
analysis of results
, 119, 120
clustering analysis
, 118–119
process
, 116–118
Verizon
, 109
corporation’s performance, evaluation of
, 111
peer groups
, 112
process
, 112, 113
Classification and regression tree (CART)
, 97
Clustering analysis
, 118–119
Correlation matrix
, 84
Cumulative mean estimation (CUME)
, 91–92
Customer retention, impact of a service failure on
, 54
Cyclic pricing
, 30
Data-level methods, for bankruptcy prediction
, 87
Decision tree
, 94–95, 97, 101, 102, 103, 104
Delays in delivery, service contracts for
, 51
model development
, 55–60
delivery time
, 56–57
loss function and due dates
, 57–58
optimal price
, 58–60
price function
, 56
product value distribution
, 55–56
results
, 60–62
Delivery time
, 56–57
truncated exponential distribution for
, 59–60
uniform distribution for
, 59
Dimension reduction, in bankruptcy prediction
, 83
computational results
, 86–89
evaluation metrics
, 87
experiment setup
, 86–87
model performances
, 88–89, 90, 91
dimension estimation
, 89–90, 91
techniques
, 84–86
Discount pricing
, 28–29
Due dates, loss function and
, 57–58
Equal pricing
, 28–29
Erlang C model
, 122
Estimation error
, 122–123
Expected loss (EL)
, 58
Expected payout
, 54, 60–61, 62
Expected revenue (ER)
, 54–55, 56, 58–59, 60
Exponential smoothing
, 4, 5, 16–17, 18–19, 20
Factor analysis (FA)
, 84, 85, 86, 88, 90–91
Fatality analysis reporting system (FARS)
, 95
Feature extraction
, 84–85
Feature selection approach
, 84–85
Financial compensation
, 109
Forecasting
business
, 121, 127, 132
sales. See Sales forecasting
Genetic algorithm (GA)
, 5–6
Great Recession
, 67, 71, 72–73, 74
Growth over time
, 69–70, 71
HGAI (hybrid of a genetic algorithm and an artificial immune system) algorithm
, 5–6
Holt-Winters model
, 4, 5
Imbalanced data
, 87, 88
classification
, 93
Individual county integrative models
, 76–79, 81
Inventory order quantity
, 28
k-nearest neighbor (KNN)
, 94–95, 97, 98, 100, 101–102, 103, 104
Least squares
, 69–70, 72
Leave-one-out cross-validation (LOOCV)
, 8, 9, 12
Linear regression
, 69, 70
Liquidity
, 111
Logistic regression
, 94–95, 97, 98, 101, 102, 103, 104
Loss function and due dates
, 57–58
Machine learning
, 4, 5, 6, 12, 15–20, 18–19, 21–22
methods, for forecasting
, 6–9
Market segmentation
of online channel
, 33, 34
in showroom strategy
, 38–39
of store channel
, 33, 34
in showroom strategy
, 37, 38
Mean absolute percentage error (MAPE)
, 12, 13–14, 15–22, 16–17, 18–19
Mixed integer programming model
, 54–55
M/M/1 queue
, 52–53
M/M/s queue
, 122
Model-building approach
, 84
Monte Carlo simulation
, 123
Moving average (MA)
, 5, 7–8, 12, 15–20, 16–17, 18–19, 21–22
Moving linear regression (MLR)
, 7–8, 12, 15–20, 21–22
Moving quadratic regression (MQR)
, 12, 15–20, 21–22
Multivariate intelligent decision-making model
, 5
Nash equilibrium
, 35, 46
Non-injured passengers and drivers in car accidents, detection of
, 93
data
, 95–96, 97
method
, 96–100
AdaBoost
, 99
decision tree
, 97
k-nearest neighbor
, 98
logistic regression
, 98
Random Forest
, 98
resampling method
, 99–100
support vector machine
, 98–99
results
, 100–104
Nonstationarity
, 122–123
North Carolina
Great Recession
, 72–73, 74
growth over time
, 69–70, 71
individual county integrative models
, 76–79, 81
integrative model
, 74–76
residuals, modeling
, 71–72
unemployment costs per capita
, 68–70, 72, 74–76, 78, 79–80, 81
Omnichannel retailing, BOPS and showroom strategy in
, 25
basic model
, 30–37
literature review
, 28–30
showroom strategy
, 37–41
Online-to-store channel
, 29–30
Optimal price
, 58–60
Oversampling
, 87
Peer group regression modeling
of AT&T CEO compensation
, 115, 116, 117
analysis of results
, 119, 120
clustering analysis
, 118–119
process
, 116–118
of Verizon CEO compensation
, 112
Personalized pricing
, 28–29
Price function
, 56
Principal component analysis (PCA)
, 84, 85, 86, 88–89, 90–92
Principal Hessian directions
, 91–92
Product value distribution
, 60–62
Profitability
, 111
Profit margin
, 111
Purchase decision tree of customers
, 31
in showroom strategy
, 37
Qualitative attributes, impact on customer satisfaction
, 52
Quantitative attributes, impact on customer satisfaction
, 52
Queueing model
, 52–53
agent-based, for call center forecasting and management optimization
, 121
bisection method
, 128–131
forecasting
, 127
model construction
, 125–127
model formulation
, 122–124
optimization criteria
, 127–128
results
, 130–131, 132
stepwise method
, 131
Radial basis function (RBF)
, 4, 5–6, 12, 13–14, 15–22
interpolation
, 8–9
Random arrival rate
, 122–123
Random Forest
, 94–95, 96–97, 98, 100, 101, 102, 103, 104
Random over-resampling (ROS)
, 99, 101, 104
Random oversampling examples (ROSE)
, 87, 94–95, 99–100, 101, 102, 104
Random under-resampling (RUS)
, 94–95, 99, 101, 104
Rare events
, 87
Regression modelling, of CEO compensation
peer group, at AT&T
, 115, 116, 117
analysis of results
, 119, 120
clustering analysis
, 118–119
process
, 116–118
of Verizon CEO compensation
, 109, 110, 111
corporation’s performance, evaluation of
, 111
peer groups
, 112
process
, 112, 113
Resampling method
, 94–95, 99–100
Residuals, modeling
, 71–72
Retailer’s newsvendor problem
, 45
Retailer System Research
, 27
Sales forecasting
, 5–6, 7–8, 9, 12, 15–20, 21–22
computational results
, 12–21
comparison of performance of sales forecasting methods
, 15–21
experimental setup
, 12–14
seasonal adjustment effect on performance of sales forecasting methods
, 13–14, 15, 16–17
literature review
, 5–6
machine learning methods
, 6–9
radial basis function interpolation
, 8–9
support vector regression
, 6–8
seasonal adjustment
, 9–11
Sales
, 111
SARIMAX with multiple linear regression (SARIMA-MLR)
, 5
Seasonal adjustment
, 9–11
effect on performance of sales forecasting methods
, 13–14, 15, 16–17
Seasonal autoregressive integrated moving average with external variables (SARIMAX)
, 5
Selective under-resampling (SUR)
, 100, 101
Service contracts, for delays in delivery
, 51
model development
, 55–60
delivery time
, 56–57
loss function and due dates
, 57–58
optimal price
, 58–60
price function
, 56
product value distribution
, 55–56
results
, 60–62
Showroom strategy
, 37–41
market segmentation of store channel in
, 37, 38
purchase decision tree of customers in
, 37
Single exponential smoothing (SES)
, 12, 16–17, 18–19, 20–22
Sliced average variance estimation (SAVE)
, 85, 86, 88, 89, 90–91
Sliced inversed regression (SIR)
, 85, 86, 88, 90–91
Solvency
, 111
Solving ratios
, 111
Stepwise method
, 131
Stepwise regression
, 84
Stock options
, 109–110
Strategic customer behavior
, 30
Sufficient dimension reduction (SDR)
, 85, 86
Support vector machine (SVM)
, 94–95, 97, 98–99, 101, 102, 103, 104
Support vector regression (SVR)
, 4, 5–8, 12, 13–14, 15–20, 18–19, 21–22
Synthetic minority oversampling technique (SMOTE)
, 87, 94–95, 99–100, 101, 102, 104
Taguchi-type loss function
, 52, 53, 54
Time series
, 4, 5–6, 8, 9, 11, 12, 15, 21–22
Trend regression models
, 4, 5
t-test
, 84
Under-sampling
, 99, 100
Unemployment costs per capita
, 68–70, 72, 74–76, 78, 79–80, 81
Waiting time
, 122–123, 124, 125, 126–127, 132
Warby Parker
, 26
- Prelims
- Section A Marketing, Sales, and Service Forecasting
- Exploring the Suitability of Support Vector Regression and Radial Basis Function Approximation to Forecast Sales of Fortune 500 Companies
- Buy-online-and-pick-up-in-store Strategy and Showroom Strategy in the Omnichannel Retailing
- Service Contracts for Delays in Delivery
- Section B Economic, Financial, and Insurance Forecasting
- Growth, Business Cycles, and the Great Recession: Comparing State and County Unemployment Costs Per Capita for North Carolina
- Dimension Reduction in Bankruptcy Prediction: A Case Study of North American Companies
- Detecting Non-injured Passengers and Drivers in Car Accidents: A New Under-resampling Method for Imbalanced Classification
- Section C CEO Compensation and Operations Forecasting
- Regression Modeling of the Peer Group of Verizon Corporation for the CEO of Verizon
- Regression Modeling Based on a Peer Group for the Executive Compensation of AT&T CEO
- Agent-based Queuing Model for Call Center Forecasting and Management Optimization
- Index