A hybrid fuzzy quantum time series and linear programming model: Special application on TAIEX index dataset

The supremacy of quantum approach is able to provide the solutions which are not practically feasible on classical machines. This paper introduces a novel quantum model for time series data which depends on the appropriate length of intervals. In this study, the effects of these drawbacks are elaborately illustrated, and some significant measures to remove them are suggested, such as use of degree of membership along with mid-value of the interval. All these improvements signify the effective results in case of quantum time series, which are verified and validated with real-time datasets.

[1]  Shyi-Ming Chen,et al.  Forecasting enrollments based on fuzzy time series , 1996, Fuzzy Sets Syst..

[2]  Pritpal Singh,et al.  A hybrid fuzzy time series forecasting model based on granular computing and bio-inspired optimization approaches , 2018, J. Comput. Sci..

[3]  Hsien-Lun Wong,et al.  Application of fuzzy time series models for forecasting the amount of Taiwan export , 2010, Expert Syst. Appl..

[4]  Amandeep Kaur,et al.  A Hybrid Algorithm Based on Particle Swarm and Spotted Hyena Optimizer for Global Optimization , 2018, SocProS.

[5]  Mu-Yen Chen,et al.  Online fuzzy time series analysis based on entropy discretization and a Fast Fourier Transform , 2014, Appl. Soft Comput..

[6]  Gaurav Dhiman,et al.  A quantum method for dynamic nonlinear programming technique using Schrödinger equation and Monte Carlo approach , 2018, Modern Physics Letters B.

[7]  Harmony Search and Nature Inspired Optimization Algorithms , 2019, Advances in Intelligent Systems and Computing.

[8]  Ching-Hsue Cheng,et al.  Fuzzy time series model based on probabilistic approach and rough set rule induction for empirical research in stock markets , 2008, Data Knowl. Eng..

[9]  Amandeep Kaur,et al.  Optimizing the Design of Airfoil and Optical Buffer Problems Using Spotted Hyena Optimizer , 2018 .

[10]  Shyi-Ming Chen,et al.  TAIEX forecasting based on fuzzy time series, particle swarm optimization techniques and support vector machines , 2013, Inf. Sci..

[11]  Pritpal Singh,et al.  High-order fuzzy-neuro-entropy integration-based expert system for time series forecasting , 2017, Neural Computing and Applications.

[12]  Pritpal Singh,et al.  High-order fuzzy-neuro expert system for time series forecasting , 2013, Knowl. Based Syst..

[13]  Pritpal Singh,et al.  An effective neural network and fuzzy time series-based hybridized model to handle forecasting problems of two factors , 2013, Knowledge and Information Systems.

[14]  Rajesh Kumar Chandrawat,et al.  An Analysis of Modeling and Optimization Production Cost Through Fuzzy Linear Programming Problem with Symmetric and Right Angle Triangular Fuzzy Number , 2016, SocProS.

[15]  Pritpal Singh,et al.  A Fuzzy-LP Approach in Time Series Forecasting , 2017, PReMI.

[16]  Pritpal Singh,et al.  Rainfall and financial forecasting using fuzzy time series and neural networks based model , 2016, International Journal of Machine Learning and Cybernetics.

[17]  B. Chissom,et al.  Forecasting enrollments with fuzzy time series—part II , 1993 .

[18]  Kun-Huang Huarng,et al.  A bivariate fuzzy time series model to forecast the TAIEX , 2008, Expert Syst. Appl..

[19]  Witold Pedrycz,et al.  Determination of temporal information granules to improve forecasting in fuzzy time series , 2014, Expert Syst. Appl..

[20]  Jian-an Fang,et al.  SYNCHRONIZATION OF TAKAGI–SUGENO FUZZY STOCHASTIC DELAYED COMPLEX NETWORKS WITH HYBRID COUPLING , 2009 .

[21]  Sangchul Won,et al.  ADAPTIVE FUZZY SYNCHRONIZATION OF TWO DIFFERENT CHAOTIC SYSTEMS WITH STOCHASTIC UNKNOWN PARAMETERS , 2010 .

[22]  Anurag Rai,et al.  An analysis of QoS ranking prediction framework techniques , 2019, Modern Physics Letters B.

[23]  Ritika Maini,et al.  DHIMAN: A novel algorithm for economic Dispatch problem based on optimization metHod usIng Monte Carlo simulation and Astrophysics coNcepts , 2019, Modern Physics Letters A.

[24]  Sen Guo,et al.  ED-SHO: A framework for solving nonlinear economic load power dispatch problem using spotted hyena optimizer , 2018, Modern Physics Letters A.

[25]  Gaurav Dhiman,et al.  A four-way decision-making system for the Indian summer monsoon rainfall , 2018, Modern Physics Letters B.

[26]  Pritpal Singh,et al.  A brief review of modeling approaches based on fuzzy time series , 2015, International Journal of Machine Learning and Cybernetics.

[27]  Witold Pedrycz,et al.  Using interval information granules to improve forecasting in fuzzy time series , 2015, Int. J. Approx. Reason..

[28]  Alexey V. Melkikh,et al.  Quantum paradoxes, entanglement and their explanation on the basis of quantization of fields , 2017 .

[29]  Hao-Tien Liu,et al.  An improved fuzzy time series forecasting method using trapezoidal fuzzy numbers , 2007, Fuzzy Optim. Decis. Mak..

[30]  Çagdas Hakan Aladag,et al.  Forecasting in high order fuzzy times series by using neural networks to define fuzzy relations , 2009, Expert Syst. Appl..

[31]  Gaurav Dhiman,et al.  A quantum approach for time series data based on graph and Schrödinger equations methods , 2018, Modern Physics Letters A.

[32]  Vijay Kumar,et al.  Astrophysics inspired multi-objective approach for automatic clustering and feature selection in real-life environment , 2018, Modern Physics Letters B.

[33]  Amandeep Kaur,et al.  A Review on Search-Based Tools and Techniques to Identify Bad Code Smells in Object-Oriented Systems , 2018, Harmony Search and Nature Inspired Optimization Algorithms.

[34]  Vijay Kumar,et al.  Spotted Hyena Optimizer for Solving Complex and Non-linear Constrained Engineering Problems , 2018, Harmony Search and Nature Inspired Optimization Algorithms.

[35]  Pritpal Singh,et al.  Uncertainty representation using fuzzy-entropy approach: Special application in remotely sensed high-resolution satellite images (RSHRSIs) , 2018, Appl. Soft Comput..

[36]  M. Andrecut.  A STATISTICAL-FUZZY PERCEPTRON , 1999 .

[37]  Sheng-Tun Li,et al.  Deterministic fuzzy time series model for forecasting enrollments , 2007, Comput. Math. Appl..

[38]  Amandeep Kaur,et al.  Spotted Hyena Optimizer for Solving Engineering Design Problems , 2017, 2017 International Conference on Machine Learning and Data Science (MLDS).

[39]  Witold Pedrycz,et al.  Effective intervals determined by information granules to improve forecasting in fuzzy time series , 2013, Expert Syst. Appl..

[40]  Alexey V. Melkikh,et al.  Nonlinearity of Quantum Mechanics and Solution of the Problem of Wave Function Collapse , 2015 .

[41]  Gaurav Dhiman,et al.  Spotted hyena optimizer: A novel bio-inspired based metaheuristic technique for engineering applications , 2017, Adv. Eng. Softw..

[42]  Ching-Hsue Cheng,et al.  A hybrid multi-order fuzzy time series for forecasting stock markets , 2009, Expert Syst. Appl..

[43]  Vijay Kumar,et al.  Multi-objective spotted hyena optimizer: A Multi-objective optimization algorithm for engineering problems , 2018, Knowl. Based Syst..

[44]  Kun-Huang Huarng,et al.  The application of neural networks to forecast fuzzy time series , 2006 .

[45]  Vijay Kumar,et al.  Emperor penguin optimizer: A bio-inspired algorithm for engineering problems , 2018, Knowl. Based Syst..

[46]  Mu-Yen Chen,et al.  A hybrid fuzzy time series model based on granular computing for stock price forecasting , 2015, Inf. Sci..

[47]  Amandeep Kaur,et al.  STOA: A bio-inspired based optimization algorithm for industrial engineering problems , 2019, Eng. Appl. Artif. Intell..

[48]  Vijay Kumar,et al.  Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems , 2019, Knowl. Based Syst..

[49]  Shivraj R. Singh,et al.  A robust method of forecasting based on fuzzy time series , 2007, Appl. Math. Comput..

[50]  Ching-Hsue Cheng,et al.  Fuzzy time-series based on Fibonacci sequence for stock price forecasting , 2007 .