Addressing big data issues using RNN based techniques. Issue 8 (17th November 2019)

• Record Type: Journal Article
• Authors: Das, Tanuja; Saha, Goutam
• Is Part Of: Journal of information & optimization sciences. Volume 40:Issue 8(2019)
• Page Start: 1773
• Page End: 1785
• Publication Date: 2019-11-17
• Subjects: Computer Science; Recurrent Neural Networks -- Long Short Term Memory -- Gated Recurrent Unit -- Time series data; Electronic data processing -- Periodicals; Information science -- Periodicals; Mathematical optimization -- Periodicals; 519.6
• DOI: 10.1080/02522667.2019.1703268
• Languages: English
• ISSNs: 0252-2667

Abstract

Abstract: Most of the real world prediction problems are naturally associated with time component which requires time series data as input. Presently, machine learning approaches are used for forecasting purpose from time series data. Though the time parameter provides more information but it is also accompanied by many problems like temporal dependence and temporal structures. Neural network based approaches has the ability to address this problem as it has the capability of automatic learning and feature extraction from raw Big data. In this paper, Recurrent Neural Networks and its variants, namely, LSTM and GRU were tried for the purpose of forecasting from time series data. The experimentation was done on Yahoo Finance data in different conditions for accessing the prediction accuracy based on hourly historical data. The resulting performance with respect to prediction accuracy was analysed. The results confirm that among RNN, LSTM and GRU, GRU has the best predictive ability in case of temporal problems.