Predicting stock market trends using machine learning and deep learning algorithms via continuous and binary data; a comparative analysis on the Tehran stock exchange
Bhuvaneshwaran S.K, Kavivendhan Ks, Sabaresan V
Stock market, Trends prediction, Classification, Machine learning, Deep learning
Nowadays, a hot challenge for supermarket chains is to offer personalized services to their customers. Stock prediction, supplying the stock user a item list for the next purchase according to her current needs, is one of these services. Current approaches are not capable of capturing at the same time the different factors influencing the stock user decision process: co-occurrence, sequential, periodicity and re-currency of the purchased items. To this aim, we define a pattern Temporal Annotated Recurring Sequence able to capture simultaneously and adaptively all these factors. We define the method to extract and develop a predictor for the next stock named that, on top, is able to understand the level of the stock user and recommend the set of most necessary items. By adopting the supermarket chains could crop tailored suggestions for each individual stock user which in turn could effectively speed up their stock prediction sessions. A deep experimentation shows that they are able to explain the stock user purchase behavior, and that TBP outperforms the state-of-the-art competitors.
Article Details
Unique Paper ID: 151314

Publication Volume & Issue: Volume 7, Issue 12

Page(s): 659 - 663
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