Stock Market Trend Prediction using Supervised Learning

被引：4

作者：

Khattak, Asad Masood ^{[1
]}

Ullah, Habib ^{[2
]}

Khalid, Hassan Ali ^{[2
]}

Habib, Ammara ^{[2
]}

Asghar, Muhammad Zubair ^{[2
]}

Kundi, Fazal Masud ^{[2
]}

机构：

[1] Zayed Univ, Coll Technol Innovat, Dubai, U Arab Emirates

[2] Gomal Univ, Inst Comp & Informat Technol ICIT, Dera Ismail Khan, Pakistan

来源：

SOICT 2019: PROCEEDINGS OF THE TENTH INTERNATIONAL SYMPOSIUM ON INFORMATION AND COMMUNICATION TECHNOLOGY | 2019年

关键词：

Trend Prediction; Machine Learning; Supervised Learning;

D O I：

10.1145/3368926.3369680

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

The stock trend prediction has received considerable attention of researchers in recent times. It is an important application in machine learning domain. In this work, we propose a machine learning based stock trend prediction system with a focus on minimizing data sparseness in the acquired datasets. We perform outlier detection on the acquired dataset for dimensionality reduction and employ K-nearest neighbor classifier for predicting stock trend. Results obtained show the effectiveness of the proposed system, when compared with baseline studies.

引用

页码：85 / 91

页数：7

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