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.
Publisher
Darcy & Roy Press Co. Ltd.
Reference10 articles.
1. 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.
2. Piccolo D. A distance measure for classifying ARIMA models[J]. Journal of time series analysis, 1990, 11(2): 153-164.
3. 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.
4. Lindberger, N. A. (1973). Stochastic identification of computer-regulated linear plants in noisy environments. International Journal of Control, 17(1), 65-80.
5. 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.