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Competitive Risk Aware Algorithm for k-min Search Problem

Iftikhar Ahmad1,*, Abdulwahab Ali Almazroi2, Mohammed A. Alqarni3, Muhammad Kashif Nawaz1
1 Department of Computer Science and Information Technology, University of Engineering and Technology, Peshawar, Pakistan
2 University of Jeddah, College of Computing and Information Technology at Khulais, Department of Information Technology, Jeddah, Saudi Arabia
3 University of Jeddah, College of Computer Science and Engineering, Department of Software Engineering, Jeddah, Saudi Arabia
* Corresponding Author: Iftikhar Ahmad. Email:

Intelligent Automation & Soft Computing 2022, 31(2), 1131-1142. https://doi.org/10.32604/iasc.2022.020715

Received 03 June 2021; Accepted 04 July 2021; Issue published 22 September 2021

Abstract

In a classical k-min search problem, an online player wants to buy k units of an asset with the objective of minimizing the total buying cost. The problem setting allows the online player to view only a single price quotation at each time step. A price quotation is the price of one unit of an asset. After receiving the price quotation, the online player has to decide on the number of units to buy. The objective of the online player is to buy the required k units in a fixed length investment horizon. Online algorithms are proposed in the literature for k-min search problem; however, these algorithms are risk averse in nature. We propose a risk aware k-min search algorithm which allows the online player to manage her risk level. The proposed algorithm is evaluated against the benchmark algorithm based on a real-world scenario using DAX30 data set. The proposed algorithm achieved up to 36.67% better results than the corresponding benchmark algorithm.

Keywords

Time series search; risk reward framework; online algorithms

Cite This Article

I. Ahmad, A. Ali Almazroi, M. A. Alqarni and M. Kashif Nawaz, "Competitive risk aware algorithm for k-min search problem," Intelligent Automation & Soft Computing, vol. 31, no.2, pp. 1131–1142, 2022.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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