A hybrid supervised semi-supervised graph-based model to predict one-day ahead movement of global stock markets and commodity prices
| Autor: | Arash Negahdari Kia, Saman Haratizadeh, Saeed Bagheri Shouraki |
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| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
business.industry Computer science Graph based General Engineering 02 engineering and technology Machine learning computer.software_genre Computer Science Applications 020901 industrial engineering & automation Artificial Intelligence Kruskal's algorithm 0202 electrical engineering electronic engineering information engineering Graph (abstract data type) 020201 artificial intelligence & image processing Artificial intelligence business computer Stock (geology) |
| Zdroj: | Expert Systems with Applications. 105:159-173 |
| ISSN: | 0957-4174 |
| DOI: | 10.1016/j.eswa.2018.03.037 |
| Popis: | 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 market’s 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. |
| Databáze: | OpenAIRE |
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