Abstract
A fundamental problem with complex time series analysis involves data prediction and repair. However, existing methods are not accurate enough for complex and multidimensional time series data. In this paper, we propose a novel approach, a complex time series prediction model, which is based on the conditional random field (CRF) and recurrent neural network (RNN). This model can be used as an upper-level predictor in the stacking process or be trained using deep learning methods. Our approach is more accurate than existing methods in some suitable scenarios, as shown in the experimental results.
Supported by The National Key Research and Development Program of China (2020YFB1006104).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Kégl, B.: The return of AdaBoost.MH: multiclass Hamming trees. arXiv preprint arXiv:1312.6086 (2013)
Liang, G., Zhang, C.: Empirical study of bagging predictors on medical data. In: Proceedings of the Ninth Australasian Data Mining Conference, vol. 121, pp. 31–40. Australian Computer Society, Inc. (2011)
Ozay, M., Vural, F.T.Y.: A new fuzzy stacked generalization technique and analysis of its performance. arXiv preprint arXiv:1204.0171 (2012)
Radosavljevic, V., Vucetic, S., Obradovic, Z.: Continuous conditional random fields for regression in remote sensing. In: ECAI, pp. 809–814 (2010)
Ristovski, K., Radosavljevic, V., Vucetic, S., Obradovic, Z.: Continuous conditional random fields for efficient regression in large fully connected graphs. In: Twenty-Seventh AAAI Conference on Artificial Intelligence (2013)
Sill, J., Takács, G., Mackey, L., Lin, D.: Feature-weighted linear stacking. arXiv preprint arXiv:0911.0460 (2009)
Smyth, P., Wolpert, D.: Linearly combining density estimators by stacking. Mach. Learn. 36(1–2), 59–83 (1999)
Xin, X., King, I., Deng, H., Lyu, M.R.: A social recommendation framework based on multiscale continuous conditional random fields. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management, pp. 1247–1256. ACM (2009)
Zhang, Y., et al.: Sequential click prediction for sponsored search with recurrent neural networks. In: Twenty-Eighth AAAI Conference on Artificial Intelligence (2014)
Zhang, Y., et al.: Sequential click prediction for sponsored search with recurrent neural networks. In: Twenty-Eighth AAAI Conference on Artificial Intelligence (2014)
Zheng, S., et al.: Conditional random fields as recurrent neural networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1529–1537 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wei, Y., Guo, H., Yang, D., Li, M., Zheng, B., Wang, H. (2023). Complex Time Series Analysis Based on Conditional Random Fields. In: Yu, Z., et al. Data Science. ICPCSEE 2023. Communications in Computer and Information Science, vol 1879. Springer, Singapore. https://doi.org/10.1007/978-981-99-5968-6_16
Download citation
DOI: https://doi.org/10.1007/978-981-99-5968-6_16
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-5967-9
Online ISBN: 978-981-99-5968-6
eBook Packages: Computer ScienceComputer Science (R0)