Research Area:  Machine Learning

Abstract:

Market prediction has been an important machine learning research topic in recent decades. A neglected issue in prediction is having a model that can simultaneously pay attention to the interaction of global markets along historical data of the target markets being predicted. As a solution, we present a hybrid supervised semi-supervised model called HyS3 for direction of movement prediction. The graph-based semi-supervised part of HyS3 models the markets global interactions through a network designed with a novel continuous Kruskal-based graph construction algorithm called ConKruG. The supervised part of the model injects results extracted from each markets historical data to the network whenever the hybrid model allows with an innovative conditional mechanism. The significance of higher prediction accuracy of HyS3 is comparing to other models is proved statistically against other models including supervised models and network-based semi-supervised predictions.

Keywords:  
Supervised
Semi-Supervised
Graph
Stock Markets
Commodity Prices
Machine Learning
Deep Learning

Author(s) Name:  Arash Negahdari Kia,Saman Haratizadeh and Saeed Bagheri Shouraki

Publisher name:  ELSEVIER

DOI:  10.1016/j.eswa.2018.03.037

Volume Information:  Volume 105, 1 September 2018, Pages 159-173

Paper Link:   https://www.sciencedirect.com/science/article/abs/pii/S0957417418301829