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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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Improved Wind Speed Prediction Using Empirical Mode Decomposition, ZHANG, Y., ZHANG, C., SUN, J., GUO, J.
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  2/2020 - 5

Deep Learning Based Prediction Model for the Next Purchase

UTKU, A. See more information about UTKU, A. on SCOPUS See more information about UTKU, A. on IEEExplore See more information about UTKU, A. on Web of Science, AKCAYOL, M. A. See more information about AKCAYOL, M. A. on SCOPUS See more information about AKCAYOL, M. A. on SCOPUS See more information about AKCAYOL, M. A. on Web of Science
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Download PDF pdficon (1,382 KB) | Citation | Downloads: 323 | Views: 506

Author keywords
time series analysis, deep learning, prediction, e-commerce

References keywords
series(26), time(25), neural(19), forecasting(16), networks(12), learning(11), prediction(9), arima(9), network(8), deep(8)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2020-05-31
Volume 20, Issue 2, Year 2020, On page(s): 35 - 44
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2020.02005
Web of Science Accession Number: 000537943500005
SCOPUS ID: 85087459081

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Time series represent the consecutive measurements taken at equally spaced time intervals. Time series prediction uses the information in a time series to predict future values. The future value prediction is important for many business and administrative decision makers especially in e-commerce. To promote business, sales prediction and sensing of future consumer behavior can help business decision makers in marketing campaigns, budget and resource planning. In this study, deep learning based a new prediction model has been developed for the time of next purchase in e-commerce. The proposed model has been extensively tested and compared with RF, ARIMA, CNN and MLP using a retail market dataset. The experimental results show that the developed model has been more successful than RF, ARIMA, CNN and MLP to predict the time of the next purchase.

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References Weight

Web of Science® Citations for all references: 2,043 TCR
SCOPUS® Citations for all references: 7,444 TCR

Web of Science® Average Citations per reference: 42 ACR
SCOPUS® Average Citations per reference: 152 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2020-11-26 15:44 in 294 seconds.

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