2018 Vol.1 Apr No.2

• Title: Time Series Prediction Model of Regional Economic Development Based on Multivariate Autoregression
• Name: Frances Prudence
• Company: University of Alaska Fairbanks, America
• Abstract:

  At present, China's regional economy is in the form of multivariate development, which makes traditional time series prediction technology unable to complete the prediction requirements for regional economic development. To this end, a time series prediction model for regional economic development based on multivariate autoregression is proposed. The autoregressive model is used to linearly predict the time series trend of regional economic development. The regression algorithm supporting multivariate is used to perform linear prediction again, and the two prediction results are combined to improve the accuracy of time series prediction. The experimental results show that the multi-variable autoregressive time series prediction model is superior to the traditional model, and the regional economic prediction effect with extremely high precision is obtained.

• Keyword: multivariate; autoregressive model; regional economy; time series prediction;
• DOI: 10.12250/jpciams2018020116
• Citation form: Frances Prudence.Time Series Prediction Model of Regional Economic Development Based on Multivariate Autoregression[J]. Computer Informatization and Mechanical System, 2018, vol. 1, pp. 18-23.

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