DEVELOPMENT OF AN IMPROVED HIDDEN MARKOV MODEL BASED FUZZY TIME SERIES FORECASTING MODEL USING GENETIC ALGORITHM

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dc.contributor.authorABBA, Bashir Baba
dc.date.accessioned2017-12-21T12:58:54Z
dc.date.available2017-12-21T12:58:54Z
dc.date.issued2016-10
dc.descriptionA Dissertation Submitted to the Department of Electrical and Computer Engineering, Ahmadu Bello University, Zaria, in Partial Fulfillment of the Requirements for the Award of Master of Science (M.Sc) Degree in Computer Engineeringen_US
dc.description.abstractThis research is aimed at the development of an improved Hidden Markov Model (HMM) based fuzzy time series (FTS) forecasting model using Genetic Algorithm (GA). In order to improve forecasting performance, a GA and HMM is developed to optimize and properly estimate membership values in the fuzzy relationship matrix in the fuzzy inference stage. Monte Carlo simulation was applied to estimate the stochastic outcome of the data and further improve the model reflection of real data and randomness. The developed model was implemented in MATLAB R2015a and tested with the Cheng and Sheng‘s data of the daily average temperature and cloud density of Taipei as a benchmark data for bivariate FTS. The performance of the proposed GA-HMM based FTS was evaluated using Mean Square Error (MSE) and the Average Forecasting Error Percentage (AFEP) as metrics. The results showed that the developed model had an MSE of 0.5976 and AFEP of 1.8673 for the bivariate benchmark HMM-FTS data of the daily average temperature and cloud density of Taipei, Taiwan as against 0.933 and 2.7464 respectively obtained from (Li and Cheng, 2012). This amounts to an improvement of 35% and 32% for the MSE and AFEP respectively. The model was also applied to forecast the short term Internet traffic data of ABU, Zaria. Simulation result shows an MSE and AFEP values of 68.32392 and 0.08904 respectively, indicating a good forecasting performance considering the large size of these traffics and their randomness. Thus, these results demonstrate both the superiority of the proposed GA-HMM based FTS model at making good forecasts considering the large sizes of these traffics and their randomness and also its robustness in adaptation to time series of different structural and statistical characteristics.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/9874
dc.language.isoenen_US
dc.subjectDEVELOPMENT,en_US
dc.subjectIMPROVED HIDDEN MARKOV MODEL,en_US
dc.subjectFUZZY TIME SERIES FORECASTING MODEL,en_US
dc.subjectGENETIC ALGORITHM,en_US
dc.titleDEVELOPMENT OF AN IMPROVED HIDDEN MARKOV MODEL BASED FUZZY TIME SERIES FORECASTING MODEL USING GENETIC ALGORITHMen_US
dc.typeThesisen_US
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