Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/28206
Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: A Quantum based Evolutionary Algorithm for Stock Index and Bitcoin Price Forecasting
Author(s): Amjad, Usman
Jilani, Tahseen Ahmed
Tariq, Humera
Hussain, Amir
Keywords: Quantum evolutionary algorithm
fuzzy time series
nature inspired computing
fuzzy logic
crypto currency
bitcoin
Issue Date: 30-Sep-2018
Citation: Amjad U, Jilani TA, Tariq H & Hussain A (2018) A Quantum based Evolutionary Algorithm for Stock Index and Bitcoin Price Forecasting. International Journal of Advanced Computer Science and Applications, 9 (9), pp. 123-132. http://thesai.org/Downloads/Volume9No9/Paper_17-A_Quantum_based_Evolutionary_Algorithm.pdf
Abstract: Quantum computing has emerged as a new dimension with various applications in different fields like robotic, cryptography, uncertainty modeling etc. On the other hand, nature inspired techniques are playing vital role in solving complex problems through evolutionary approach. While evolutionary approaches are good to solve stochastic problems in unbounded search space, predicting uncertain and ambiguous problems in real life is of immense importance. With improved forecasting accuracy many unforeseen events can be managed well. In this paper a novel algorithm for Fuzzy Time Series (FTS) prediction by using Quantum concepts is proposed in this paper. Quantum Evolutionary Algorithm (QEA) is used along with fuzzy logic for prediction of time series data. QEA is applied on interval lengths for finding out optimized lengths of intervals producing best forecasting accuracy. The algorithm is applied for forecasting Taiwan Futures Exchange (TIAFEX) index as well as for Bitcoin crypto currency time series data as a new approach. Model results were compared with many preceding algorithms.
URL: http://thesai.org/Downloads/Volume9No9/Paper_17-A_Quantum_based_Evolutionary_Algorithm.pdf
Rights: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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