时间序列预测论文推荐：经典与前沿研究

时间序列预测是一个广泛研究的领域，有许多经典的论文。以下是一些值得参考的时间序列预测论文：

Box, G. E. P., & Jenkins, G. M. (1970). Time series analysis: forecasting and control. Holden-Day.
Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: principles and practice. OTexts.
Chen, C., & Liu, L. M. (1993). Joint estimation of model parameters and outlier effects in time series. Journal of the American Statistical Association, 88(421), 284-297.
Wang, L., & Wang, D. (2018). Time series forecasting using deep learning. International Journal of Forecasting, 34(4), 606-621.
Lai, K. K., & Wang, S. (2000). Neural-network-based adaptive control for nonlinear discrete-time systems with time-varying delays. IEEE Transactions on Neural Networks, 11(6), 1373-1383.
Taylor, J. W. (2008). An evaluation of methods for very short-term time series forecasting. International Journal of Forecasting, 24(3), 453-468.
Makridakis, S., & Hibon, M. (2000). The M3-competition: results, conclusions and implications. International Journal of Forecasting, 16(4), 451-476.
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). 'Why should i trust you?': Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1135-1144).
Salinas, D., & Salinas, E. (2014). Time series forecasting using a hybrid ARIMA and neural network model. Journal of Computational and Applied Mathematics, 270, 384-390.
Zhang, G. P., Patuwo, B. E., & Hu, M. Y. (1998). Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting, 14(1), 35-62.

这些论文涵盖了时间序列预测的各个方面，包括统计方法、机器学习方法和深度学习方法等。可以根据自己的研究兴趣和需求选择相应的论文进行深入阅读。