Abstract
In order to analyze the driving factors of innovation and entrepreneurship, based on the time series analysis algorithm, this paper combines the analysis requirements of innovation and entrepreneurship driving factors to improve the time series, uses decomposition methods to decompose the complex original data into relatively simple components and reconstruct them, and predicts the reconstructed components to integrate the final predicted value. Moreover, this paper introduces entrepreneurial attitude as an intermediary variable and verifies it through data collection and statistical analysis, so that entrepreneurial traits influence entrepreneurial propensity through entrepreneurial attitude. The test results show that entrepreneurial attitude can better explain the influence of entrepreneurial traits on entrepreneurial propensity. In addition, this paper constructs an analysis model of driving factors for innovation and entrepreneurship, obtains experimental data through questionnaire survey methods, and conducts experimental research in combination with mathematical statistics. From the statistical results, it can be seen that the innovative and entrepreneurial driving factor analysis model based on time series analysis proposed in this paper is effective.
Highlights
From ancient times to the present, the phenomenon of entrepreneurship has always existed, but it has not received everyone’s attention
We mainly focus on Least Squares Support Vector Regression (LS-support vector regression model (SVR)), which is widely used and faster to solve in MFCM
The purpose of this paper is to explore the relationship between entrepreneurial traits, entrepreneurial attitude, and entrepreneurial propensity
Summary
From ancient times to the present, the phenomenon of entrepreneurship has always existed, but it has not received everyone’s attention. In view of the phenomenon that the selection of subjects may cause deviations in the research results, the subjects selected in this study are subjects with higher education, knowledge, and culture, rich work and practical experience, entrepreneurial capabilities, and mature thoughts, especially MBA. This will broaden the scope of research subjects, increase the accuracy of data, and make empirical research more convincing, but at the same time enrich the content of entrepreneurial theory and expand the scope of research on entrepreneurial theory. This paper analyzes the driving forces of innovation and entrepreneurship based on time series algorithms, and on this basis, it studies the problems of innovation and proposes relevant strategies
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