Short term stock selection with case-based reasoning technique

2014 ◽  
Vol 22 ◽  
pp. 205-212 ◽  
Author(s):  
Huseyin Ince
Author(s):  
Norehan Zulkiply ◽  
Jennifer S Burt

Purpose – The present study investigated whether or not the benefits of interleaving of exemplars from several categories vary with retention interval in inductive learning.   Methodology – Two experiments were conducted using paintings (Experiment 1) and textual materials (Experiment 2), and the experiments used a mixed factorial design. Forty students participated in each experiment for course credit. In each experiment, participants studied a series of exemplars from several categories which were presented massed and interleaved, and later their induction was tested either shortly after the study phase (short-term retention) or after a week’s delay (long- term retention).   Findings – Consistent with findings from previous studies, the interleaving effect was found in the short-term retention condition, and crucially, the present study provided the initial evidence that interleaving of exemplars also affected long-term retention. Interestingly, massing was judged to be more effective than spacing (interleaving) in most groups, even when actual performance showed the opposite.   Significance – The present study shows that interleaved exemplars have considerable potential in improving inductive learning in the long term. For example, induction is used in case-based reasoning which requires one to start with learning from specifi c cases, and then form generalizations of these cases by identifying the commonalities between them. In order to enhance long-term retention, educators may want to consider using interleaved presentation rather than massed presentation in teaching examples or cases from a particular category or concept.


Author(s):  
Stephan Rudolph

Abstract In the field of case-based reasoning (CBR), the derivation of so-called ‘similarity measures’ is an unresolved open question. In this work dimensional analysis is used to derive appropriate similarity conditions for a CBR technique. For the subclass of all case descriptions consisting of real-valued quantities with physical units, it is shown how the Pi-Theorem can be used to construct similarity conditions from these case descriptions. Within this approach a proof for the correctness of the CBR technique can be derived. The properties of the CBR technique using dimensionless groups indicate that it bears some potential in engineering design, where knowledge in the form of analytical equations is not always available. Often only pointwise and/or incomplete knowledge about the future design object in the form of previous designs, prototypes or simulation results is available and appears in certain cases to be sufficient for the new CBR technique.


Mathematics ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 1727
Author(s):  
Victor Zakharov ◽  
Yulia Balykina ◽  
Ovanes Petrosian ◽  
Hongwei Gao

Because of the lack of reliable information on the spread parameters of COVID-19, there is an increasing demand for new approaches to efficiently predict the dynamics of new virus spread under uncertainty. The study presented in this paper is based on the Case-Based Reasoning method used in statistical analysis, forecasting and decision making in the field of public health and epidemiology. A new mathematical Case-Based Rate Reasoning model (CBRR) has been built for the short-term forecasting of coronavirus spread dynamics under uncertainty. The model allows for predicting future values of the increase in the percentage of new cases for a period of 2–3 weeks. Information on the dynamics of the total number of infected people in previous periods in Italy, Spain, France, and the United Kingdom was used. Simulation results confirmed the possibility of using the proposed approach for constructing short-term forecasts of coronavirus spread dynamics. The main finding of this study is that using the proposed approach for Russia showed that the deviation of the predicted total number of confirmed cases from the actual one was within 0.3%. For the USA, the deviation was 0.23%.


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