Improving Time Series Models Prediction Based On Empirical Mode Decomposition Using Stock Market Data

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dc.contributor.authorMohammad Raquibul Hossain
dc.date.accessioned2022-06-30T17:13:18Z
dc.date.available2022-06-30T17:13:18Z
dc.date.issued2021-10
dc.description.abstractTime series analysis and prediction is a very important and active research area. In this age of profuse data generation, proper use of available data has become crucial in forecasting and decision making. This thesis presents the research study involving the development of five advanced forecasting methods and their experimentation on twelve stock price time series datasets. Traditional forecasting methods have limitations in forecasting potentiality due to their linearity and stationarity assumptions on the datasets. However, real life data including stock price data have sophisticated features and patterns encompassing nonlinearity and non-stationarity. Therefore, there is the research scope to search for better methods to improve forecast accuracy obtainable from the traditional methods by applying advanced approaches. Empirical mode decomposition (EMD), a very essentially important part of Hilbert-Huang transforms (HHT) is a very adaptive decomposition algorithm to view data from granular and different time scales. Being a robust analysing tool in signal processing, EMD has been widely applied in other fields including economics and finance. However, there are still scopes in improving the forecast accuracy of nonlinear nonstationary financial time series using EMD and other forecasting methods. From such relevant hypotheses, this study was followed by three research objectives. Five EMD-based methods were developed on these objectivesen_US
dc.identifier.urihttp://hdl.handle.net/123456789/15527
dc.publisherUniversiti Sains Malaysia.en_US
dc.subjectImproving Time Series Models Predictionen_US
dc.subjectEmpirical Mode Decompositionen_US
dc.subjectImproving Timeen_US
dc.titleImproving Time Series Models Prediction Based On Empirical Mode Decomposition Using Stock Market Dataen_US
dc.typeThesisen_US
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