Gold Price Forecasting: A Novel Approach Based on Text Mining and Big-Data-Driven Model

被引：0

作者：

Kian Poor, Saeed ^{[1
]}

Fattahi, Shahram ^{[2
]}

Hajian, Mohsen ^{[1
]}

机构：

[1] Payame Noor Univ, Fac Econ, Tehran, Iran

[2] Razi Univ, Fac Econ, Kermanshah, Iran

来源：

STUDIES IN BUSINESS AND ECONOMICS | 2024年 / 19卷 / 03期

关键词：

gold price Prediction; Trends; Online News; Data Mining; CNN; CONVOLUTIONAL NEURAL-NETWORKS; VOLATILITY; PREDICTION; INTEGRATION;

D O I：

10.2478/sbe-2024-0049

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

This work uses Google Trends and online text mining to test a novel approach to gold price prediction. To explain the gold price prognosis, the convolutional neural network (CNN) also uses linguistic criteria for news items about gold. The findings of this study can be used to explain the theoretical underpinnings of information processing worldwide. According to experimental findings, big data forecasting and online data mining are more efficient than other methods. As a result, utilizing 19926 in sum news headlines acquired, the synergistic connection between news items and Google Trends is successful in accurately projecting the price of gold.

引用

页码：156 / 171

页数：16

共 50 条

[31] The research on natural gas demand forecasting model based on data mining laws [J].

Gao Jian ;

Dong Xiucheng .

TIRMDCM 2007: PROCEEDINGS OF THE FIRST INTERNATIONAL CONFERENCE ON TECHNOLOGY INNOVATION, RISK MANAGEMENT AND SUPPLY CHAIN MANAGEMENT, VOLS 1 AND 2, 2007, :608-611

[32] A novel hybrid deep learning model for taxi demand forecasting based on decomposition of time series and fusion of text data [J].

Zhu, Kun ;

Zhang, Shuai ;

Zhang, Wenyu ;

Zhang, Zhiqiang .

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2021, 41 (02) :3355-3371

[33] Text Mining-based Approach for Forecasting Spare Parts Demand of K-X Tanks [J].

Kim, Jaedong .

2018 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND ENGINEERING MANAGEMENT (IEEE IEEM), 2018, :1652-1656

[34] Research on The Construction of Corruption Early Warning Mining Model Based on Big Data [J].

Cao, Qi ;

Zhang, Yaowen .

2022 INTERNATIONAL CONFERENCE ON BIG DATA, INFORMATION AND COMPUTER NETWORK (BDICN 2022), 2022, :132-138

[35] Forecasting Price Volatility Range of Crude Palm Oil by Mining the Historical Data Using Hybrid Range Model [J].

Sim, Kwan Hua ;

Goh, Isaac Yung Shen ;

Sim, Kwa N. Yong ;

Tan, Yiing Chee .

INTELLIGENT SYSTEMS AND APPLICATIONS (ICS 2014), 2015, 274 :531-540

[36] Day-Ahead Crude Oil Price Forecasting Using a Novel Morphological Component Analysis Based Model [J].

Zhu, Qing ;

He, Kaijian ;

Zou, Yingchao ;

Lai, Kin Keung .

SCIENTIFIC WORLD JOURNAL, 2014,

[37] Data-Driven Precision by a Novel Fusion of Deer Hunting Optimization and LSTM Networks for Stock Price Forecasting [J].

Li, Yun-Zhang ;

Liu, Fang-Fang ;

Yu, Yong-Hao .

NATIONAL ACADEMY SCIENCE LETTERS-INDIA, 2024,

[38] Breaking through the limitation of carbon price forecasting: A novel hybrid model based on secondary decomposition and nonlinear integration [J].

Lan, Yuqiao ;

Huangfu, Yubin ;

Huang, Zhehao ;

Zhang, Changhong .

JOURNAL OF ENVIRONMENTAL MANAGEMENT, 2024, 362

[39] The data-driven approach as an operational real-time flood forecasting model [J].

Phuoc Khac-Tien Nguyen ;

Chua, Lloyd Hock-Chye .

HYDROLOGICAL PROCESSES, 2012, 26 (19) :2878-2893

[40] Forecasting directional bitcoin price returns using aspect-based sentiment analysis on online text data [J].

Loginova, Ekaterina ;

Tsang, Wai Kit ;

van Heijningen, Guus ;

Kerkhove, Louis-Philippe ;

Benoit, Dries F. .

MACHINE LEARNING, 2024, 113 (07) :4761-4784

← 1 2 3 4 5 →