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Economic Data Forecasting Through Interval Data Analysis

Yan Cheng

https://orcid.org/0009-0006-7514-6372

School of Economics and Management, Chuzhou University, Chuzhou 239000, P. R. China

E-mail Address: chengyan1502@chzu.edu.cn

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and

Jinwen Su

https://orcid.org/0009-0001-6566-2617

College of Machinery and Electrical Engineering, Chuzhou University, Chuzhou 239000, P. R. China

E-mail Address: hbsjw1002@outlook.com

Corresponding author.

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https://doi.org/10.1142/S0218213024400025Cited by:1 (Source: Crossref)

This article is part of the issue:

Special Issue on Heterogeneous Domain Adaptation Learning and its Applications in Intelligent System
Guest Editors: Fa Zhu, Xiaochun Cheng and Sotiris Kotsiantis

Abstract

As an important reflection of the national economy system, stock market is closely related to the development of a country, which has received widespread attention of researchers in the economics. With the daily trading of the stock market, stock price forecasting has gradually been one of the common concerns in the economic analysis. Compared with traditional forecasting task, the stock price is interval data which can be handled by interval data regression or multi-output regression. Previous stock forecasting merely considers the stock price in homogeneous scenarios. However, the price distributions from different stocks may be heterogeneous. It is a challenging task to analyze the relationship between different stocks which follow heterogeneous distributions. In order to forecast stocks in heterogeneous scenarios, this paper introduces multi-output transfer learning into stock price forecasting. Compared with traditional regression or multi-output regression models, the multi-output transfer regression can predict opening price, closing price, highest price and lowest price of stocks and utilize source domain of a known stock to enhance the prediction of target stock price which may have limited known data in training set. The experimental results on four public market indices demonstrate the effectiveness of multi-output transfer regression for stock price forecasting.

Keywords:

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