1. Caballero, K., Akella, R., 2015. Dynamically modeling Patient’s health state from electronic medical records: A time series approach. Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Min. 2015-Augus, 69–78.
2. Support vector machine with adaptive parameters in financial time series forecasting;Cao;IEEE Trans. Neural Networks,2003
3. Chang, Y.-Y., Sun, F.-Y., Wu, Y.-H., Lin, S.-D., 2018. A Memory-Network Based Solution for Multivariate Time-Series Forecasting. arXiv preprint arXiv:1809.02105.
4. Chen, T., Yin, H., Chen, H., Wu, L., Wang, H., Zhou, X., Li, X., 2018. TADA: Trend Alignment with Dual-Attention Multi-task Recurrent Neural Networks for Sales Prediction, in: 2018 IEEE Int. Conf. on Data Mining (ICDM). 49–58.
5. Chung, J., Gulcehre, C., Cho, K., Bengio, Y., 2014. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. arXiv preprint arXiv:1412.3555.