Open AccessResearch on Industry Prosperity Index Forecast based on Hidden Markov Model
Author(s) -
Jing Xu,
Haiyang Li,
Yuyang Chen,
Qi Chen
Publication year - 2022
Publication title -
bcp business and management
Language(s) - English
Resource type - Journals
ISSN - 2692-6156
DOI - 10.54691/bcpbm.v17i.390
Subject(s) - prosperity , consumption (sociology) , index (typography) , hidden markov model , sorting , electricity , production (economics) , markov chain , computer science , key (lock) , warning system , predictive power , power consumption , electric power industry , markov process , markov model , econometrics , environmental economics , economics , power (physics) , artificial intelligence , engineering , microeconomics , machine learning , economic growth , computer security , algorithm , statistics , telecommunications , mathematics , social science , sociology , world wide web , electrical engineering , philosophy , epistemology , quantum mechanics , physics
The research is mainly based on the electricity consumption information of 800 key energy-consuming enterprises. By cleaning and sorting the data, high-frequency structured electricity consumption data is obtained, which is based on the production theory in economics. Using complex network models and Hidden Markov Algorithm constructs an industry prosperity index based on power operation data by industry, so as to objectively reflects the operation of various industries and plays a predictive and early warning role in economy.