[1A5-GS-2] Machine learning: stock price prediction

[1A5-GS-2-03] Exhaustive Analysis of Concept Drift

〇Kenichi YOSHIDA¹ (1. Universty of Tsukuba)

Keywords:Concept drift, Drift detection

Many supervised deep neural networks have been studied to predict stock prices.
Among these studies, recent methods used the attention mechanism to extract relevant time-series data and improve prediction accuracy.
Although the advantage of the attention mechanism has been demonstrated based on various experimental results,
relationship between these studies and concept drift studies have not been discussed.
This paper discusses this relationship.
Coexistence and transition of multiple concepts and exhaustive analysis of concept drift are discussed in this paper.