An optimization in big data time series prediction method by Parzen estimation with Spark

Research Article
Open access

An optimization in big data time series prediction method by Parzen estimation with Spark

Hao Liu 1*
  • 1 Yangzhou Unversity    
  • *corresponding author 1766601430@qq.com
Published on 8 December 2023 | https://doi.org/10.54254/2753-8818/18/20230276
TNS Vol.18
ISSN (Print): 2753-8826
ISSN (Online): 2753-8818
ISBN (Print): 978-1-83558-201-5
ISBN (Online): 978-1-83558-202-2

Abstract

With the development and change of big data related technologies, more and more large amounts of data need to be analyzed. Now there are companies like Google, Yahoo, etc. Frameworks such as MapReduce, Hadoop, Spark, etc. are developed for processing large amounts of data. In this paper, relevant discussions and researches are carried out on time series forecasting under the new era of big data. Now there are time series forecasting methods based on map reduce, Hadoop, spark data processing framework, including nearest neighbor distribution method, neural network method, etc., which have made quite good achievements in big data time series forecasting. By reading the relevant research literature, it is universally acknowledged that the Spark’s framework has good application prospects and potential in predicting big data time series. As a result, this paper is mainly aimed at the optimization and improvement of the big data time series forecasting method on the basis of the spark framework. The author noticed that most of the default configurations of spark clusters are generated by default or automatically, rather than the optimal solution obtained after algorithm optimization, so there is still room for improvement in this regard. In this regard, this paper proposes a kernel method for visual data processing of related configurations and parameters, and then optimizes the default data configuration as much as possible to improve the accuracy and feasibility of the big data time series prediction method on the basis of the spark framework. In this paper, the optimized scheme is used to forecast the domestic electricity consumption in the past five years, and the results show that the optimized scheme has a good improvement performance on the basis of the original method.

Keywords:

Spark, time series forecast, big data, Parzen estimation

Liu,H. (2023). An optimization in big data time series prediction method by Parzen estimation with Spark. Theoretical and Natural Science,18,10-18.
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References

[1]. J. Dean, S. Ghemawat, Mapreduce: simplified data processing on large clusters, Commun. ACM 51 (1) (2008) 107–113.

[2]. T. White, Hadoop, The Definitive Guide, O’ Really Media, 2012.

[3]. M. Hamstra, H. Karau, M. Zaharia, A. Knwinski, P. Wendell, Learning Spark: Lightning-Fast Big Analysis, O’ Really Media, 2015.

[4]. J.C. Riquelme-Santos, R. González-Cámpora, A study of the suitability of auto-encoders for preprocessing data in breast cancer experimentation, J. Biomed. Inform. 72 (2017) 33–44.

[5]. E. Florido, F. Martínez-Álvarez, A. Morales-Esteban, J. Reyes, J. Aznarte-Mellado, Detecting precursory patterns to enhance earthquake prediction in chile, Comput. Geosci. 76 (Supplement C) (2015) 112–120.

[6]. G. Asencio-Cortés, E. Florido, A. Troncoso, F. Martínez-Álvarez, A novel metho-dology to predict urban traffic congestion with ensemble learning, Soft. Comput. 20(11) (2016) 4205–4216.

[7]. A. M. Fernández, J.F. Torres, A. Troncoso, F. Martínez-Álvarez, Automated spark clusters deployment for big data with standalone applications integration, Lect. Notes Artif. Intell. 9868 (2016) 150–159.

[8]. R. Talavera-Llames,R. Pérez-Chacón,A. Troncoso,F. Martínez-Álvarez. Big data time series forecasting based on nearest neighbours distributed computing with Spark[J]. Knowledge-Based Systems,2018,161.

[9]. N. Hamid, V. Chang, R.J. Walters, G.B. Wills, A multi-core architecture for a hybrid information system, Comput. Electr. Eng. (2017)

[10]. G. U. Yule, On a method of investigating periodicities in disturbed series, with special reference to wolfer’s sunspot numbers, Philos. Trans. R. Soc. Lond. Ser. A Math. Phys. Eng. Sci. (1927).

[11]. C. W. Tsai, C.F. Lai, H.C. Chao, A. Vasilakos, Big data analytics: a survey, J. Big Data 2 (1) (2015) 21.

[12]. L. Zhou, S. Pan, J. Wang, A.V. Vasilakos, Machine learning on big data: opportunities and challenges, Neurocomputing 237 (2017) 350–361.

[13]. T. Do, F. Poulet, Random local SVMs for classifying large datasets, in: Proceedings of the International Conference on Future Data and Security Engineering, 2015, pp. 3–15.

[14]. J. González-López, S. Ventura, A. Cano, Distributed nearest neighbor classification for large-scale multi-label data on spark, Future Gener. Comput. Syst. 87 (2018) 66–82.

[15]. R. Talavera-Llames, R. Pérez-Chacón, M. Martínez-Ballesteros, A. Troncoso, F. Martínez-Álvarez, A nearest neighbours-based algorithm for big time series data forecasting, in: Proceedings of the International Conference on Hybrid Artificial Intelligence Systems, 2016, pp. 174–185.

[16]. Asgari Marjan,Yang Wanhong,Farnaghi Mahdi. Spatiotemporal data partitioning for distributed random forest algorithm: Air quality prediction using imbalanced big spatiotemporal data on spark distributed framework[J]. Environmental Technology & Innovation,2022,27.

[17]. Ramkuma M.P,Reddy P.V. Bhaskar, Thirukrishna J.T., Vidyadhari Ch.. Intrusion detection in big data using hybrid feature fusion and optimization enabled deep learning based on spark architecture[J].Computers & Security,2022(prepublish).

Cite this article

Liu,H. (2023). An optimization in big data time series prediction method by Parzen estimation with Spark. Theoretical and Natural Science,18,10-18.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2nd International Conference on Computing Innovation and Applied Physics

ISBN：978-1-83558-201-5(Print) / 978-1-83558-202-2(Online)
Editor：Marwan Omar, Roman Bauer
Conference website: https://www.confciap.org/
Conference date: 25 March 2023
Series: Theoretical and Natural Science
Volume number: Vol.18
ISSN：2753-8818(Print) / 2753-8826(Online)

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References

[1]. J. Dean, S. Ghemawat, Mapreduce: simplified data processing on large clusters, Commun. ACM 51 (1) (2008) 107–113.

[2]. T. White, Hadoop, The Definitive Guide, O’ Really Media, 2012.

[3]. M. Hamstra, H. Karau, M. Zaharia, A. Knwinski, P. Wendell, Learning Spark: Lightning-Fast Big Analysis, O’ Really Media, 2015.

[4]. J.C. Riquelme-Santos, R. González-Cámpora, A study of the suitability of auto-encoders for preprocessing data in breast cancer experimentation, J. Biomed. Inform. 72 (2017) 33–44.

[5]. E. Florido, F. Martínez-Álvarez, A. Morales-Esteban, J. Reyes, J. Aznarte-Mellado, Detecting precursory patterns to enhance earthquake prediction in chile, Comput. Geosci. 76 (Supplement C) (2015) 112–120.

[6]. G. Asencio-Cortés, E. Florido, A. Troncoso, F. Martínez-Álvarez, A novel metho-dology to predict urban traffic congestion with ensemble learning, Soft. Comput. 20(11) (2016) 4205–4216.

[7]. A. M. Fernández, J.F. Torres, A. Troncoso, F. Martínez-Álvarez, Automated spark clusters deployment for big data with standalone applications integration, Lect. Notes Artif. Intell. 9868 (2016) 150–159.

[8]. R. Talavera-Llames,R. Pérez-Chacón,A. Troncoso,F. Martínez-Álvarez. Big data time series forecasting based on nearest neighbours distributed computing with Spark[J]. Knowledge-Based Systems,2018,161.

[9]. N. Hamid, V. Chang, R.J. Walters, G.B. Wills, A multi-core architecture for a hybrid information system, Comput. Electr. Eng. (2017)

[10]. G. U. Yule, On a method of investigating periodicities in disturbed series, with special reference to wolfer’s sunspot numbers, Philos. Trans. R. Soc. Lond. Ser. A Math. Phys. Eng. Sci. (1927).

[11]. C. W. Tsai, C.F. Lai, H.C. Chao, A. Vasilakos, Big data analytics: a survey, J. Big Data 2 (1) (2015) 21.

[12]. L. Zhou, S. Pan, J. Wang, A.V. Vasilakos, Machine learning on big data: opportunities and challenges, Neurocomputing 237 (2017) 350–361.

[13]. T. Do, F. Poulet, Random local SVMs for classifying large datasets, in: Proceedings of the International Conference on Future Data and Security Engineering, 2015, pp. 3–15.

[14]. J. González-López, S. Ventura, A. Cano, Distributed nearest neighbor classification for large-scale multi-label data on spark, Future Gener. Comput. Syst. 87 (2018) 66–82.

[15]. R. Talavera-Llames, R. Pérez-Chacón, M. Martínez-Ballesteros, A. Troncoso, F. Martínez-Álvarez, A nearest neighbours-based algorithm for big time series data forecasting, in: Proceedings of the International Conference on Hybrid Artificial Intelligence Systems, 2016, pp. 174–185.

[16]. Asgari Marjan,Yang Wanhong,Farnaghi Mahdi. Spatiotemporal data partitioning for distributed random forest algorithm: Air quality prediction using imbalanced big spatiotemporal data on spark distributed framework[J]. Environmental Technology & Innovation,2022,27.

[17]. Ramkuma M.P,Reddy P.V. Bhaskar, Thirukrishna J.T., Vidyadhari Ch.. Intrusion detection in big data using hybrid feature fusion and optimization enabled deep learning based on spark architecture[J].Computers & Security,2022(prepublish).

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