Review of the evolution of statistical models and deep learning models: Based on the perspective of ARIMA and SVM

Authors

  • Yimeng Wang

DOI:

https://doi.org/10.54097/hset.v70i.13870

Keywords:

ARIMA; SVM; causal inference; parameter optimization.

Abstract

The importance of the evolution of statistical models and deep learning models, such as autoregressive integrated moving average model (ARIMA) and support vector machine (SVM), has drawn much attention from scholars. This paper investigates the evolution of statistical models and deep learning models based on the perspective of ARIMA and SVM. Then, this paper finds that there are some limitations of the literature on ARIMA and SVM, including less attention to causal inference, the scarce application of the future option, and the deep exploration of parameter optimization.

Downloads

Download data is not yet available.

References

Livieris, Ioannis E., Emmanuel Pintelas, and Panagiotis Pintelas. "A CNN–LSTM model for gold price time-series forecasting." Neural computing and applications 32 (2020): 17351-17360.

Piccolo D. A distance measure for classifying ARIMA models[J]. Journal of time series analysis, 1990, 11(2): 153-164.

Kalpakis K, Gada D, Puttagunta V. Distance measures for effective clustering of ARIMA time-series[C]//Proceedings 2001 IEEE international conference on data mining. IEEE, 2001: 273-280.

Lindberger, N. A. (1973). Stochastic identification of computer-regulated linear plants in noisy environments. International Journal of Control, 17(1), 65-80.

Martinez, E. Z., Silva, E. A. S. D., & Fabbro, A. L. D. (2011). A SARIMA forecasting model to predict the number of cases of dengue in Campinas, State of São Paulo, Brazil. Revista da Sociedade Brasileira de Medicina Tropical, 44, 436-440.

Chadsuthi, S., Modchang, C., Lenbury, Y., Iamsirithaworn, S., & Triampo, W. (2012). Modeling seasonal leptospirosis transmission and its association with rainfall and temperature in Thailand using time–series and ARIMAX analyses. Asian Pacific journal of tropical medicine, 5(7), 539-546.

Li, Y., Shao, X., & Cai, W. (2007). A consensus least squares support vector regression (LS-SVR) for analysis of near-infrared spectra of plant samples. Talanta, 72(1), 217-222.

Li, S., Lei, W., Zhang, W., Wang, X., & Wang, L. (2020). Weighted TSVR based nonlinear channel frequency response estimation for MIMO-OFDM system. IEEE Access, 8, 224283-224291.

Lin, K. P., & Pai, P. F. (2010). A fuzzy support vector regression model for business cycle predictions. Expert Systems with Applications, 37(7), 5430-5435.

Yong Yu, Xiaosheng Si, Changhua Hu, Jianxun Zhang; A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures. Neural Comput 2019; 31 (7): 1235–1270. doi: https://doi.org/10.1162/neco_a_01199

Downloads

Published

15-11-2023

How to Cite

Wang, Y. (2023). Review of the evolution of statistical models and deep learning models: Based on the perspective of ARIMA and SVM. Highlights in Science, Engineering and Technology, 70, 339-342. https://doi.org/10.54097/hset.v70i.13870