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Economic forecasting is a scientific decision-making tool, and it is one of the important basis for the government to formulate economic plans, predict the implementation of the plan, and guide the implementation of the plan. Current knowledge about the use of online news in the prediction of economic patterns in China is limited, especially considering the spatio-temporal dynamics over time. This study explored the spatio-temporal patterns of economic output values in Yinzhou, Ningbo, China between 2018 and 2021, and proposed generalized linear model (GLM) and Geographically weighted regression (GWR) model to predict the dynamics using online news data. The results indicated that there were spatio-temporal variations in the economic dynamics in the study area. The online news showed a great potential to predict economic dynamics, with better performance in the GWR model. The findings suggested online news combining with spatio-temporal approach can better forecast economic dynamics, which can be seen as a pre-requisite for developing an online news-based surveillance system The advanced spatio-temporal analysis enables governments to garner insights about the patterns of economic dynamics over time, which may enhance the ability of government to formulate economic plans and to predict the implementation of the plan. The proposed model may be extended to greater geographic area to validate such approach.

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The manufacturing industry is an important pillar of the national economy. It is of vital importance to develop statistical modellings in order to quantify the relationship between potential internal drivers and the trend of output values in the manufacturing industry. However, only a few statistical modellings have been established to investigate such associations. This study developed the correlation coefficient model and generalized linear model (GLM) to measure the single and interactive effects of the internal drivers on the changes of the output values. For the GLM, different predictive variables were developed to fit into the dataset, and the performance of the models were compared using fitness parameters. Furthermore, an industry survey dataset for 1,180 manufacturing enterprises in 2020 was used to validate the models. The use of the GLM combining land area, number of employees, scientific research input, and labor productivity may have a great potential to bolster capacity in monitoring and predicting the trend of output values in the manufacture industry.