Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Prediction of stock market price using hybrid of wavelet transform and artificial neural network Subject The topic of the resource Artificial neural network; Discrete Wavelet Transform (DWT); Time series and stock market prediction Description An account of the resource Background/Objectives: Accurate prediction of stock market is highly challenging. This paper presents a forecasting model based on Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN) for predicting financial time series. Methods/Statistical analysis: The idea of forecasting stock market prices with discrete wavelet transform is the central element of this paper. The proposed forecasting model uses the Discrete Wavelet Transform to decompose the financial time series data. The obtained approximation and detail coefficients after decomposition of the original time series data are used as input variables of back propagation neural network to forecast future stock prices. Approximation coefficients can characterize the coarse structure of the data and detail coefficients capture ruptures, discontinuities and singularities in the original data, to recognize the long-term trends in the original data. Findings: The proposed model was applied to five datasets. For all of the datasets, accuracy measures showed that the presented model outperforms a conventional model. It also proved that the hybrid forecasting technique has achieved better results compared with the approach which is not using the wavelet transform. Applications/Improvements: The accuracy of the proposed hybrid method can also be improved by developing a model using artificial neural network with Adaptive Neuro Fuzzy Interference System. Creator An entity primarily responsible for making the resource Chandar S.K.; Sumathi M.; Sivanandam S.N. Source A related resource from which the described resource is derived Indian Journal of Science and Technology, Vol-9, No. 8 Publisher An entity responsible for making the resource available Indian Society for Education and Environment Date A point or period of time associated with an event in the lifecycle of the resource 2016-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.17485/ijst/2016/v9i8/87905" target="_blank" rel="noreferrer noopener">https://doi.org/10.17485/ijst/2016/v9i8/87905</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-84961990702&amp;doi=10.17485%2Fijst%2F2016%2Fv9i8%2F87905&amp;partnerID=40&amp;md5=cc3ed64f826a1bf9ad0e40616099e3dd" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-84961990702&amp;doi=10.17485%2fijst%2f2016%2fv9i8%2f87905&amp;partnerID=40&amp;md5=cc3ed64f826a1bf9ad0e40616099e3dd</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 9746846 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Chandar S.K., Christ University, Bangalore, Karnataka, 560029, India; Sumathi M., Sri Meenakshi Government College for Arts for Women (Autonomous), Madurai, Tamil Nadu, 625002, India; Sivanandam S.N., Karpagam College of Engineering, Coimbatore, Tamil Nadu, 641032, India