Tests of the efficiency of racetrack betting using bookmaker odds
Management Science
Original Contribution: Stacked generalization
Neural Networks
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Machine Learning
Computational Statistics & Data Analysis - Nonlinear methods and data mining
An introduction to variable and feature selection
The Journal of Machine Learning Research
Mining with rarity: a unifying framework
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
A tutorial on support vector regression
Statistics and Computing
Forecasting stock market movement direction with support vector machine
Computers and Operations Research
Getting the Most Out of Ensemble Selection
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
The design features of forecasting support systems and their effectiveness
Decision Support Systems
Focusing on non-respondents: Response modeling with novelty detectors
Expert Systems with Applications: An International Journal
A hybrid model for exchange rate prediction
Decision Support Systems
Exploring Decision Makers Use of Price Information in a Speculative Market
Management Science
The class imbalance problem: A systematic study
Intelligent Data Analysis
Application of wrapper approach and composite classifier to the stock trend prediction
Expert Systems with Applications: An International Journal
Financial time series prediction using polynomial pipelined neural networks
Expert Systems with Applications: An International Journal
Integrating GA-based time-scale feature extractions with SVMs for stock index forecasting
Expert Systems with Applications: An International Journal
Regularized least squares fuzzy support vector regression for financial time series forecasting
Expert Systems with Applications: An International Journal
Boosting and measuring the performance of ensembles for a successful database marketing
Expert Systems with Applications: An International Journal
A fuzzy GARCH model applied to stock market scenario using a genetic algorithm
Expert Systems with Applications: An International Journal
The impact of estimation error on the dynamic order admission policy in B2B MTO environments
Expert Systems with Applications: An International Journal
Integration of heterogeneous models to predict consumer behavior
Expert Systems with Applications: An International Journal
GMRVVm-SVR model for financial time series forecasting
Expert Systems with Applications: An International Journal
Building comprehensible customer churn prediction models with advanced rule induction techniques
Expert Systems with Applications: An International Journal
Predicting stock returns by classifier ensembles
Applied Soft Computing
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Using Gaussian process based kernel classifiers for credit rating forecasting
Expert Systems with Applications: An International Journal
Knowledge discovery using neural approach for SME's credit risk analysis problem in Turkey
Expert Systems with Applications: An International Journal
Using data mining to improve assessment of credit worthiness via credit scoring models
Expert Systems with Applications: An International Journal
An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction
Expert Systems with Applications: An International Journal
Forecasting model selection through out-of-sample rolling horizon weighted errors
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
We study forecasting applications where the response variable is heavily correlated with one or a small set of covariates which we term dominant predictors. Dominant predictors commonly occur in financial forecasting where future market prices are heavily influenced by current prices, and to a much lesser degree, by many other, more subtle factors such as weather or calendar effects. We hypothesize that dominating predictors may mask the influence of the subtle factors, reducing forecasting accuracy. Consequently, we argue that it is crucial to find means of accurately accounting for the effect of the subtle factors on the response variable. To achieve this we present a two-stage modeling methodology which postpones the introduction of dominating predictors into the model building process until all predictive value from the other covariates has been extracted. To confirm our hypothesis and to test the effectiveness of the two-stage approach, we conduct an empirical study related to forecasting the outcome of sports events, which are well known to exhibit dominating predictors. Our results confirm that especially complex, nonlinear models are vulnerable to the masking effect and benefit from the two-stage paradigm. Our findings have important implications for forecasters who operate in environments where the influence of some predictors on the variable being forecast exceeds those of other covariates by a wide margin and we demonstrate appropriate ways to approach such forecasting tasks.