Index
Advances in Business and Management Forecasting
ISBN: 978-1-83982-091-5, eISBN: 978-1-83982-090-8
ISSN: 1477-4070
Publication date: 1 September 2021
This content is currently only available as a PDF
Citation
(2021), "Index", Lawrence, K.D. and Klimberg, R.K. (Ed.) Advances in Business and Management Forecasting (Advances in Business and Management Forecasting, Vol. 14), Emerald Publishing Limited, Leeds, pp. 167-171. https://doi.org/10.1108/S1477-407020210000014012
Publisher
:Emerald Publishing Limited
Copyright © 2021 by Emerald Publishing Limited
INDEX
Accuracy
balanced
, 42, 45
brand
, 107
capability
, 88–89
cluster analysis
, 72
forecasting
, 13, 15, 107–108
model accuracy
, 108
performance measures
, 41
Autoregressive integrated moving average (ARIMA)
, 103–104, 108, 111, 116
Balanced accuracy
, 42, 45
Balance sheet measurement
, 26
Binary target variable
, 52
Candidate models
, 40
Cash flow measurement
, 26
Closed-end fund discount (CEFD)
, 5
Cluster analysis method
, 72–73
cubic clustering criterion (CCC)
, 74–75
fleet maintenance data set
, 72
inverse distance weighted (IDW)
, 74
sum of squares error (SSE)
, 74–75
Collaborative planning
, 102
Commercialization
, 150, 152, 155–156
Compensation committee
, 24
Consumer sentiment
, 4–5
Corporate valuation measurement
, 25
COVID-19 pandemic
, 35–36
commercialization
, 150, 152, 155–156
international price
, 152, 155–156, 159
Peru. See Peru, COVID-19 pandemic
technological industry
, 155–156
COVID-19 treatment efficiency, China
data envelopment analysis (DEA) method
, 120–122
dynamic trend analysis
, 142–143
efficiency analysis
, 125–146
first-stage efficiency
, 144
management implications
, 143–146
policy suggestions
, 143–146
second-stage efficiency
, 145
two-stage efficiency analysis
, 126–127, 131–132, 136–137, 141–142
Cubic clustering criterion (CCC)
, 75
Cubic smoothing spline
, 103–104
Data cleaning
, 105
Data envelopment analysis (DEA) method
serial two-stage model
, 123–125
traditional model
, 122–123
Demand forecasting
, 29–30
Demand management
, 101–102
Demand planning
, 102
Dependent variable
, 7
Desirable values
, 95
Digitization
, 157–158
Double exponential smoothing
, 103–104
Earnings per share (EPS)
, 8–10
E-commerce
, 152
Economic/financial indicators
, 6
Economic indicator variables
, 8
Emergency rooms (ERs)
, 35–36
Equity markets
, 7
False positive rate (FPR)
, 53–54
Federal Reserve Economic Database (FRED)
, 7
Financial market performance
, 4–5
Financial market variables
, 8–10
Firm value (FV)
, 3–4
Fleet maintenance data set
, 72–73
Food inflation
, 163
Forecasting accuracy
accuracy by method
, 107–108
autoregressive integrated moving average (ARIMA)
, 103–104
collaborative planning
, 102
cubic smoothing spline
, 103–104
data cleaning
, 105
demand management
, 101–102
demand planning
, 102
double exponential smoothing
, 103–104
future research
, 108–109
long-term seasonal random trend (LTSRT)
, 103–104
pandemic’s effect
, 102
random walk with drift
, 103–104
seasonal random trend (SRT)
, 103–104
seasonal random walk (SRW)
, 103–104
simple autoregression
, 103–104
simple exponential smoothing
, 103–104
simple linear regression
, 103–104
simple polynomial regression
, 103–104
specific stock-keeping unit (SKU)
, 102–103
time series
, 103
trend and seasonality
, 106
Forecasting data
, 30–31
Gamma distribution
, 88–89
Gradient boosting
, 12
Gross domestic product (GDP)
, 4–5, 164–165
5G technology
, 158
Home Mortgage Disclosure Act website (HMDA)
, 54
Huawei
, 154, 158
Income statement
, 26
Independent variables
, 7
Intellectual property
, 156–157
International price
, 152, 155–156, 159, 164
Inverse distance weighted (IDW)
, 76–77, 79
Investor sentiment
, 6–7
Length of stay (LOS)
balanced accuracy
, 42
candidate models
, 40
COVID-19
, 35–36
distribution of patients
, 37
emergency rooms (ERs)
, 35–36
performance measurement
, 41–42
predictive models
, 40
random oversampling (ROS)
, 36
random undersampling (RUS)
, 36, 42
resampling methods
, 40–41
Rhode Island Hospital Discharge Data
, 36
RUS Boost
, 40
SMOTE
, 40–41
true negative rate
, 41
true positive rate
, 41
variable importance
, 44
variables descriptions
, 39
Veterans Health Administration (VHA)
, 36
Long-term seasonal random trend (LTSRT)
, 103–104
Lower bound
, 98
Lower specification limit (LSL)
, 89–90, 92
Manager sentiment
, 6–7
Market capitalization
, 6
Market sentiment
, 3–4
closed-end fund discount (CEFD)
, 5
consumer sentiment
, 4–5
data
, 7–11
dependent variable
, 7
economic/financial indicators
, 6
economic indicator variables
, 8
financial market performance
, 4–5
financial market variables
, 8–10
gross domestic product (GDP)
, 4–5
independent variables
, 7
investor sentiment
, 6–7
manager sentiment
, 6–7
market capitalization
, 6
personal consumption expenditure (PCE)
, 8
Purchasing Manager’s Index (PMI)
, 10
sentiment report variables
, 10–11
simple moving averages (SMAs)
, 8–10
US Treasury Note
, 8–10
Volatility Index (VIX)
, 8–10
Mean absolute deviation (MAD)
, 31, 80, 83
Mean absolute percentage error (MAPE)
, 80, 83
Mean absolute percent error (MAPE)
, 31
K-means clustering
, 73–74
Mean square error (MSE)
, 80, 83
MetLife
balance sheet measurement
, 26
cash flow measurement
, 26
compensation committee
, 24
corporate valuation measurement
, 25
definition
, 24
income statement
, 26
management effectiveness
, 25–26
peer companies of
, 24–25
peer group
, 24
regression model
, 24, 26
Model development
gradient boosting
, 12
neural network
, 13
random forest
, 11–12
Model seasonality
, 31
Modified undersampling method
, 56
Monthly forecasting
, 31
Moving average (MA) forecasts
definition
, 76
estimation methods
, 80–83
interpolation adjustments
, 76–77
inverse distance weighted (IDW) interpolated values
, 76–77, 79
mean absolute deviation (MAD)
, 80–83
mean absolute percentage error (MAPE)
, 80–83
mean square error (MSE)
, 80–83
Multilateral trading system
, 151–152
Multilayer perceptron (MLP)
, 13
Multiple linear regression (MLR)
cluster analysis method
, 72–73
cubic clustering criterion (CCC)
, 75
estimation methods
, 80–83
fleet maintenance data set
, 72–73
future research
, 83–84
interpolation adjustments
, 76–77
inverse distance weighted (IDW)
, 79
mean absolute deviation (MAD)
, 80, 83
mean absolute percentage error (MAPE)
, 80, 83
K-means clustering
, 73–74
mean square error (MSE)
, 80, 83
moving average forecasts
, 76
sum of squares error (SSE)
, 75
Neural network
, 13
Non-normal distributions
, 88–89
Normality distribution
, 88–89
Pandemic’s effect
, 102
Peer companies
, 24–25
Peer group
, 24
Performance measurement
, 41–42
Performance metrics
, 53–54
Personal consumption expenditure (PCE)
, 8
Peru, COVID-19 pandemic
commodity prices
, 162
economic impact
, 162–164
exchange rate
, 162
food inflation
, 163
food insecurity
, 163
gross domestic product (GDP)
, 164–165
international price
, 164
Predictive models
, 40
different resampling data
, 56–60
imbalanced classification, performance metrics
, 53–54
Principal component analysis
, 89
Process capability measurement
aggregate performance measure
, 92
desirable values
, 95
gamma distribution
, 88–89
individual characteristics
, 90–91, 97
lower bound
, 98
lower specification limit (LSL)
, 89–90, 92
multiple characteristics
, 92–95
non-normal distributions
, 88–89
normality distribution
, 88–89
performance
, 88
principal component analysis
, 89
process performance
, 89
product characteristics
, 96
upper bound
, 98
upper specification limit (USL)
, 89–90, 92
Process performance
, 89
Product characteristics
, 96
Providence-Warwick data
, 54–55
Purchasing Manager’s Index (PMI)
, 10
Random forest
, 11–12
Random oversampling (ROS)
, 36, 52–53
Random oversampling examples (ROSE)
, 52–53, 55–57, 60, 63, 66
Random undersampling (RUS)
, 36, 42, 52–53
Receiver operating characteristic (ROC) curve
, 53–54
Regression model
, 24, 26
Resampling imbalanced data
, 55–56
Resampling methods
, 40–41
Resampling techniques
class imbalanced ratio
, 65–66
C-undersampling vs. random undersampling
, 60–65
future research
, 67–68
imbalanced classification
, 60–65
performance metrics
, 60–65
predictive models
, 56, 60, 65
Rhode Island Hospital Discharge Data
, 36
RUS Boost
, 40
Seasonal products forecasting
analysis
, 31
demand forecasting
, 29–30
forecasting data
, 30–31
mean absolute deviation (MAD)
, 31
mean absolute percent error (MAPE)
, 31
model seasonality
, 31
monthly forecasting
, 31
trend
, 31
weekly forecasting
, 32–33
Seasonal random trend (SRT)
, 103–104
Seasonal random walk (SRW)
, 103–104
Sentiment report variables
, 10–11
Simple moving averages (SMAs)
, 8–10
Small and medium-sized enterprises (SMEs)
, 155
S&P 500 (SPX)
, 7
Specific stock-keeping unit (SKU)
, 102–103
Standard and Poor’s (S&P) 500 index
, 7
Sum of squares error (SSE)
, 75
Synthetic minority oversampling technique (SMOTE)
, 40–42, 44, 52, 55–56, 58–59, 66
Technology transfer
, 156–157
Time series
, 103
Traditional finance theory
, 3–4
True negative rate (TNR)
, 41
True positive rate (TPR)
, 41, 52
United States
China
, 150–152, 154–156
digitization
, 157–158
e-commerce
, 152
intellectual property
, 156–157
multilateral trading system
, 151–152
technology transfer
, 156–157
telephone companies global shipments
, 154
Upper bound
, 98
Upper specification limit (USL)
, 89–90, 92
US Treasury Note
, 8–10
Variables
descriptions
, 39
importance
, 44
Veterans Health Administration (VHA)
, 36
Volatility Index (VIX)
, 8–10
Weekly forecasting
, 32–33
Weighted nonconformance cost
, 93, 97
ZTE Corporation
, 154, 158
- Prelims
- Part A Forecasting Methods and Applications I
- The Predictive Power of Investor Sentiment on US Equity Market Performance
- A Regression Model to Forecast the Compensation of Top Level Executives at MetLife
- Forecasting Seasonal Products: A Case of Ice-Cream Sales
- Predicting the Length of Stay in Hospital Emergency Rooms in Rhode Island
- Part B Forecasting Methods and Applications II
- Effects of Resampling Techniques on Imbalanced Data Classification: A New Under-resampling Method
- Development of a Method to Improve Statistical Forecasts Using Interpolation and Cluster Analysis
- Performance Measures of Products with Multiple Characteristics Utilizing Unit Costs of Nonconformance
- Competing Forecasting Techniques Produces Different Optimal Solutions Based on the Product
- Part C COVID-19 Trend Analysis
- Exploring the Trend of COVID-19 Treatment Efficiency in Chinese Provinces in 2020 by Using DEA and Regression Analysis
- The Impact of International Price on the Technological Industry in the United States and China during Times of Crisis: Commercial War and COVID-19
- Price Variation in Lower Goods as of Previous Economic Crisis and the Contrast of the Current Price Situation in the Context of COVID-19 in Peru
- Index