-
CiteScore
1.08
Impact Factor
IECE Transactions on Intelligent Systematics, 2024, Volume 1, Issue 1: 10-18

Free Access | Research Article | 25 May 2024 | Cited: 2
1 Department of Computer Science, Swansea University, Swansea SA1 8EN, United Kingdom
2 School of Computer Science and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China
* Corresponding author: Hanchi Ren, email: hanchi.ren@swansea.ac.uk
Received: 05 January 2024, Accepted: 19 May 2024, Published: 25 May 2024  

Abstract
To predict future trends based on the data from sensors is an important technology for many applications, such as the Internet of Things, smart cities, etc. Based on the predicted results, further decisions and system controls can be made. Raw sensor data sets are often complex non-linear data with noise, which results in the difficulty of accurate prediction. This paper proposes a distributed deep prediction network based on a covariance intersection (CI) fusion algorithm in which the deep learning networks, such as long-term and short-term memory networks (LSTM) and gated recurrent unit networks (GRU) are fused by CI fusion algorithm to effectively develop the performance of prediction. Moreover, the variance is obtained to value the prediction results. The model is validated on the real weather dataset in Beijing. The experiments show that LSTM and GRU have their pros and cons for different data, CI fusion can develop the accuracy of the final predictions, and the entire framework has robust prediction results with a reasonable estimated variance.

Graphical Abstract
Deep Prediction Network Based on Covariance Intersection Fusion for Sensor Data

Keywords
Deep Prediction Network
Covariance Intersection (CI) Fusion
Sensor Data Analytics

References

[1]Zhang, G. P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159-175.

[2]Thissen, U. V. B. R., Van Brakel, R., De Weijer, A. P., Melssen, W. J., & Buydens, L. M. C. (2003). Using support vector machines for time series prediction. Chemometrics and intelligent laboratory systems, 69(1-2), 35-49.

[3]Tay, F. E., & Cao, L. (2001). Application of support vector machines in financial time series forecasting. omega, 29(4), 309-317.

[4]Mikolov, T., Karafiát, M., Burget, L., Cernocký, J., & Khudanpur, S. (2010, September). Recurrent neural network based language model. In Interspeech (Vol. 2, No. 3, pp. 1045-1048).

[5]Mikolov, T., Kombrink, S., Burget, L., Černocký, J., & Khudanpur, S. (2011, May). Extensions of recurrent neural network language model. In 2011 IEEE international conference on acoustics, speech and signal processing (ICASSP) (pp. 5528-5531). IEEE.

[6]Connor, J. T., Martin, R. D., & Atlas, L. E. (1994). Recurrent neural networks and robust time series prediction. IEEE transactions on neural networks, 5(2), 240-254.

[7]Gers, F. A., Schmidhuber, J., & Cummins, F. (2000). Learning to forget: Continual prediction with LSTM. Neural computation, 12(10), 2451-2471.

[8]Fu, R., Zhang, Z., & Li, L. (2016, November). Using LSTM and GRU neural network methods for traffic flow prediction. In 2016 31st Youth academic annual conference of Chinese association of automation (YAC) (pp. 324-328). IEEE.

[9]Malhotra, P., Vig, L., Shroff, G., & Agarwal, P. (2015, April). Long Short Term Memory Networks for Anomaly Detection in Time Series. In Esann (Vol. 2015, p. 89).

[10]Che, Z., Purushotham, S., Cho, K., Sontag, D., & Liu, Y. (2018). Recurrent neural networks for multivariate time series with missing values. Scientific reports, 8(1), 6085.

[11]Yao, S., Hu, S., Zhao, Y., Zhang, A., & Abdelzaher, T. (2017, April). Deepsense: A unified deep learning framework for time-series mobile sensing data processing. In Proceedings of the 26th international conference on world wide web (pp. 351-360).

[12]Osogami, T., Kajino, H., & Sekiyama, T. (2017, July). Bidirectional learning for time-series models with hidden units. In International Conference on Machine Learning (pp. 2711-2720). PMLR.

[13]Wu, Y., & Tan, H. (2016). Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework. arXiv preprint arXiv:1612.01022.

[14]Liu, Y., Zheng, H., Feng, X., & Chen, Z. (2017, October). Short-term traffic flow prediction with Conv-LSTM. In 2017 9th international conference on wireless communications and signal processing (WCSP) (pp. 1-6). IEEE.

[15]Du, S., Li, T., Yang, Y., & Horng, S. J. (2019). Deep air quality forecasting using hybrid deep learning framework. IEEE Transactions on Knowledge and Data Engineering, 33(6), 2412-2424.

[16]Kim, S., Hong, S., Joh, M., & Song, S. K. (2017). Deeprain: Convlstm network for precipitation prediction using multichannel radar data. arXiv preprint arXiv:1711.02316.

[17]Garcia, F., Mirbach, B., Ottersten, B., Grandidier, F., & Cuesta, A. (2010, September). Pixel weighted average strategy for depth sensor data fusion. In 2010 IEEE International Conference on Image Processing (pp. 2805-2808). IEEE.

[18]Rojo, J., Rivero, R., Romero-Morte, J., Fernández-González, F., & Pérez-Badia, R. (2017). Modeling pollen time series using seasonal-trend decomposition procedure based on LOESS smoothing. International journal of biometeorology, 61, 335-348.

[19]Li, S., Kwok, J. T., & Wang, Y. (2002). Multifocus image fusion using artificial neural networks. Pattern recognition letters, 23(8), 985-997.

[20]Qiu, X., Ren, Y., Suganthan, P. N., & Amaratunga, G. A. (2017). Empirical mode decomposition based ensemble deep learning for load demand time series forecasting. Applied soft computing, 54, 246-255.

[21]Pu, L., Feng, X., & Hou, Z. (2019, May). Covariance intersection fusion for visual tracking with hierarchical features. In Tenth International Conference on Graphics and Image Processing (ICGIP 2018) (Vol. 11069, pp. 1158-1166). SPIE.

[22]Niehsen, W. (2002, July). Information fusion based on fast covariance intersection filtering. In Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002.(IEEE Cat. No. 02EX5997) (Vol. 2, pp. 901-904). IEEE.

[23]Hurley, M. B. (2002, July). An information theoretic justification for covariance intersection and its generalization. In Proceedings of the fifth international conference on Information Fusion. FUSION 2002.(IEEE Cat. No. 02EX5997) (Vol. 1, pp. 505-511). IEEE.

[24]Julier, S., & Uhlmann, J. K. (2017). General decentralized data fusion with covariance intersection. In Handbook of multisensor data fusion (pp. 339-364). CRC Press.

[25]Gupta, M., & Gupta, B. (2018, August). An ensemble model for breast cancer prediction using sequential least squares programming method (slsqp). In 2018 eleventh international conference on contemporary computing (IC3) (pp. 1-3). IEEE.


Cite This Article
APA Style
Ren, H., Wang, Y., & Ma, H. (2024). Deep Prediction Network Based on Covariance Intersection Fusion for Sensor Data. IECE Transactions on Intelligent Systematics, 1(1), 10–18. https://doi.org/10.62762/TIS.2024.136898

Article Metrics
Citations:

Crossref

2

Scopus

2

Web of Science

2
Article Access Statistics:
Views: 8581
PDF Downloads: 735

Publisher's Note
IECE stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions
IECE or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
IECE Transactions on Intelligent Systematics

IECE Transactions on Intelligent Systematics

ISSN: 2998-3355 (Online) | ISSN: 2998-3320 (Print)

Email: jinxuebo@btbu.edu.cn

Portico

Portico

All published articles are preserved here permanently:
https://www.portico.org/publishers/iece/

Copyright © 2024 Institute of Emerging and Computer Engineers Inc.