Abstract
Macroeconomic situation is the overall performance of a country’s and regional economic situation. At present, the vast majority of macroeconomic indicators are obtained through sampling surveys, step-by-step reporting, statistical calculations, and other processes, which are publicly released by the Statistical Bureau. There are some shortcomings, such as lag and non-authenticity. Timely forecasting and early warning of macroeconomic trends are the important needs of government affairs. However, the timeliness of data has a direct impact on government decision-making. In this paper, the high frequency and relatively accurate big data sources are adopted to construct a multivariate regression prediction model for traditional national economic accounting indicators (such as industrial value added above the scale of Hefei), which is different from the traditional time series prediction model such as ARIMA model. Based on the macroeconomic prediction model of time series big data, multi-latitude data sources, sequential update, verification set screening model and other strategies are used to provide more reliable, timely, and easy-to-understand forecasting values of national economic accounting indicators. At the same time, the potential influencing factors of macroeconomic indicators are excavated to provide data and theoretical basis for macroeconomic analysis and decision-making.
Similar content being viewed by others
References
Abdul, R.N.A., Khamis, A., Abdullah, M.A.A.: ARIMA and VAR modeling to forecast Malaysian economic growth. J. Sci. Technol. 9(3), 16–24 (2017)
Jiang, S., Ferreira, J., Gonzalez, M.C.: Activity-based human mobility patterns inferred from mobile phone data: a case study of Singapore. IEEE Trans. Big Data 3(2), 208–219 (2017)
Joshua, B., Cadamuro, G., On, R.: Predicting poverty and wealth from mobile phone metadata. Science 350(6264), 1073–1076 (2015)
Leng, C., Wang, S.: Hidden Markov model for predicting the turning points of GDP fluctuation. In: International Conference on Future Computer and Communication Engineering (2014)
Li, H.: District GDP prediction based on ARMA model group. In: The 5th International Institute of Statistics and Management Engineering Symposium, pp. 586–591 (2012)
Li, G., Yin, G.: Application of process neural network on consumer price index prediction. Affect. Comput. Intell. Interact. 137, 427–432 (2012)
Li, J., Li, P., Liu, L.: The development of integrated simulation model for prediction. In: Control and Decision Conference, CCDC’09 Chinese, IEEE, pp. 4813–4816 (2009)
Long, G.: GDP prediction by support vector machine trained with genetic algorithm. In: The 2nd International Conference on IEEE Signal Processing Systems (ICSPS), vol. 3, p. V3-1 (2010)
Song, M., Xi, B.: Study of Xiamen’s economic growth based on the stochastic time series model. In: The 2nd IEEE International Conference on Advanced Computer Control, pp. 549–555 (2010)
Wang, X.: The application of ARMA model in forecasting the CPI of Guangdong Province. In: International Conference on Information Technology and Industrial Engineering, pp. 397–400 (2010)
Xiao, J., Wu, Y., Wang, Q., Liao, L., Hu, H.: A hybrid sales forecasting method based on stable seasonal pattern models and BPNN. In: 2007 IEEE International Conference on IEEE Automation and Logistics, pp. 2868–2872 (2007)
Yu, J., Tang, C., Long, C.: Using Holt-Winters model to forecast total retail sales of social consumer goods. In: The 4th International Institute of Statistics and Management Engineering Symposium, pp. 972–976 (2011)
Zhou, Z.: Predicting research about macroeconomic variable. In: 2009 International Institute of Applied Statistics Studies, pp. 1–5 (2009)
Acknowledgements
We would like to thank the anonymous reviewers for their comments and suggestions which greatly improve the manuscript. The work is supported by the NSF of China (No. 11871447), and Anhui Initiative in Quantum Information Technologies (AHY150200).
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Yang, Y., Kong, J., Yang, L. et al. Sequential Big Data-Based Macroeconomic Forecast for Industrial Value Added. Commun. Math. Stat. 7, 445–457 (2019). https://doi.org/10.1007/s40304-019-00177-4
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40304-019-00177-4