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Prediction of Enterprise Economic Activity Behavior Based on Neural Network and ARIMA Hybrid Model
Author(s) -
Zeyu Lin,
Shuai Li
Publication year - 2021
Publication title -
mobile information systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.346
H-Index - 34
eISSN - 1875-905X
pISSN - 1574-017X
DOI - 10.1155/2021/4571840
Subject(s) - autoregressive integrated moving average , computer science , commodity , context (archaeology) , production (economics) , audit , variety (cybernetics) , big data , artificial neural network , industrial organization , artificial intelligence , business , time series , economics , machine learning , finance , data mining , accounting , microeconomics , paleontology , biology
Enterprise economy refers to the comprehensive situation reflected in the gross product, production scale, total production and efficiency, technological content, marketing means, and so on; under certain social conditions, enterprises use resources obtained by law to engage in economic activities. Under the guidance of consciousness or culture, enterprises use “legally obtained resources” to promote economic development. Enterprise economy is affected by manpower, capital, management, operation, policy, and other aspects. In the context of the rapid development of big data in the current era, this paper proposes a prediction model of enterprise economic activity behavior based on neural network and ARIMA by investigating a variety of artificial intelligence models and verifies its feasibility. Commodity circulation enterprises have a more urgent demand for the development of business audit due to their operation characteristics. Therefore, this paper takes commodity circulation enterprises as representatives and predicts business audit in the big data environment based on the model proposed in this paper.

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