Original Contribution: Stacked generalization
Neural Networks
Evolutionary Computation in Economics and Finance
Evolutionary Computation in Economics and Finance
How to shift bias: Lessons from the baldwin effect
Evolutionary Computation
Genetic algorithms and artificial life
Artificial Life
A bootstrap evaluation of the effect of data splitting on financial time series
IEEE Transactions on Neural Networks
Constraint-based sequential pattern mining: the consideration of recency and compactness
Decision Support Systems
Mining product maps for new product development
Expert Systems with Applications: An International Journal
Mining customer knowledge for product line and brand extension in retailing
Expert Systems with Applications: An International Journal
Ontology-based data mining approach implemented for sport marketing
Expert Systems with Applications: An International Journal
Mathematics and Computers in Simulation
A new approach to the rule-base evidential reasoning: Stock trading expert system application
Expert Systems with Applications: An International Journal
Mining customer knowledge to implement online shopping and home delivery for hypermarkets
Expert Systems with Applications: An International Journal
A stock trading expert system based on the rule-base evidential reasoning using Level 2 Quotes
Expert Systems with Applications: An International Journal
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A crucial issue related to data mining on time-series is that of training period duration. The training horizon used impacts the nature of rules obtained and their predictability over time. Longer training horizons are generally sought, in order to discern sustained patterns with robust training data performance that extends well into the predictive period. However, in dynamic environments patterns that persist over time may be unavailable, and shorter-term patterns may hold higher predictive ability, albeit with shorter predictive periods. Such potentially useful shorter-term patterns may be lost when the training duration covers much longer periods. Too short a training duration can, of course, be susceptible to over-fitting to noise. We conduct experiments using different training horizons with daily-data for the S&P500 index and report the sensitivity of the performance of the obtained rules with respect to the training durations. We show that while the performance of the rules in the training period is important for inducing the "best" rules, it is not indicative of their performance in the test-period and propose alternative measures that can be used to help identify the appropriate training durations.