A novel distance-based moving average model for improvement in the predictive accuracy of financial time series

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dc.authoridOzel, Selma Ayse/0000-0001-9201-6349
dc.contributor.authorEjder, Ugur
dc.contributor.authorOzel, Selma Ayse
dc.date.accessioned2025-01-06T17:36:59Z
dc.date.available2025-01-06T17:36:59Z
dc.date.issued2024
dc.description.abstractTime-series forecasting is essential for system analysis. Many traditional studies have paid attention to individual stock-oriented solutions and disregarded general approaches on financial time series or skipped the dynamics of the system and its triggering components. It is difficult to fully adapt to evolving market conditions with stable financial indicators. For this reason, the proposed novel distance-based exponential moving-average (DBEMA) model is dynamically designed to overcome the changing conditions of financial time series. A novel distancebased moving-average feature model can produce an adaptive prediction approach for financial time series. To evaluate the impact of the novel proposed DBEMA features, they are compared to the features selected by recursive feature elimination using classification and regression trees among the financial indicators, using benchmark classification models. To confirm the performance of the proposed novel distance-based movingaverage features, the forecasting results of the features are compared using linear regression, bagged trees regressor, Gaussian naive Bayes, k-nearest neighbors, random forests, multilayer perceptron, convolutional neural network, long short-term memory, gated recurrent unit, and relative strength index method benchmark models. The experimental analysis has shown that methods with our proposed novel DBEMA features has better forecasting accuracy with respect to the methods without DBEMA. Therefore, the proposed novel distance-based moving-average methodology designed for financial time-series analysis demonstrates that it guides a new perspective in nonlinear time-series trends.
dc.identifier.doi10.1016/j.bir.2024.01.011
dc.identifier.endpage397
dc.identifier.issn2214-8450
dc.identifier.issn2214-8469
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85184757487
dc.identifier.scopusqualityQ1
dc.identifier.startpage376
dc.identifier.urihttps://doi.org/10.1016/j.bir.2024.01.011
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2068
dc.identifier.volume24
dc.identifier.wosWOS:001207350500001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofBorsa Istanbul Review
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241211
dc.subjectClassification
dc.subjectConvolutional neural network
dc.subjectDistance-based moving average
dc.subjectFeature selection
dc.subjectFinancial time -series prediction
dc.titleA novel distance-based moving average model for improvement in the predictive accuracy of financial time series
dc.typeArticle

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