Short-Term Economic Forecasting by Complex-Valued Autoregressions

Author:

Svetunkov Sergey G.1ORCID

Affiliation:

1. Peter the Great St. Petersburg Polytechnic University, St. Petersburg

Abstract

One of the directions that can expand the instrumental base for modeling the economy is complex-valued economics – ​a section of economic and mathematical modeling devoted to the use of models and methods of the theory of the function of a complex variable in economics. The article discusses the possibility of short-term economic forecasting using autoregressive models of complex variables. A classification of possible modifications of complex-valued autoregressive models is given, and the main properties of each of the classes of these models are shown. One of the varieties of these complex-valued models uses current and past errors of approximation, which means that it can be compared with the widely used model of autoregressive real variables ARIMA(p, d, q). The article makes such a comparison, both on a theoretical level and on a practical example.

Publisher

RPO for the Promotion of Institutes DE RAS

Subject

General Medicine

Reference13 articles.

1. СSvetunkov I. S. (2011). Short-term forecasting of socio-economic processes using a model with correction. BIZNES-INFORM, no. 5 (4), pp. 109–112 (in Russian).

2. Svetunkov S. G., Svetunkov I. S. (2019). Production functions of complex variables: Economic and mathematical modeling of production dynamics. Edition 2, add. Moscow: Lenand. 170 p. (in Russian).

3. Svetunkov S. G. (2020a). Forecasting economic dynamics using complex-valued autoregression with a time component (CTAR). Modern Economics: Problems and Solutions, no. 9, pp. 21–30 (in Russian).

4. Svetunkov S. G. (2020b). Complex-valued autoregression in economic forecasting of one-dimensional series. Economics of Contemporary Russia, no. 4 (91), pp. 51–62 (in Russian). DOI: 10.33293/1609-1442-2020-4(91)-51-62

5. Baryev D., Konovalov I., Voinov N. (2019). New approach to feature generation by complex-valued econometrics and sentiment analysis for stock-market prediction. In: Arseniev D., Overmeyer L., Kälviäinen H., Katalinić B. (eds). Cyber-Physical Systems and Control. CPS&C Lecture Notes in Networks and Systems, 2019, vol. 95, pp. 573–582.

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3