Deep LSTM-based Automated Learning Environment using Smart Data to Improve Awareness and Education in Time Series Forecasting
Zair Bouzidi, Abdelmalek Boudries and Mourad Amad
Published: November 11, 2021.
DOI: 10.55162/MCMS.2021.01.034
Abstract
This proposed commercial model is based on deep Long Short-Term Memory Network (LSTM), used because of its ability to learn long term dependencies, in taking the concatenated function and market data as input, while integrating Smart Data encapsulations, retrieving information by combining various search results (all the Web). It provides some good ideas for that we have extended to improve Corporate Marketing and Business Strategies. We show that the proposed model learns to both localize and recognize their different aspects. We evaluate it on the challenging task of detecting Fraud in Financial Services and Financial Time Series Forecasting and show that it is both more accurate than the state-of-the-art of other neural networks and uses fewer parameters and less computation.
Keywords: Awareness; Business; Education; LSTM; Smart Data