Systematic Literature Review on Multivariate Analysis: Research Trends, Methods and Frameworks
Systematic Literature Review on Multivariate Analysis: Research Trends, Methods and Frameworks
Keywords:
Statistics, Multivariate Analysis, Systematic Literature Review, Bibliometric Analysis.
Abstract
The focus of this research is to look at the development of analytical multivariate statistical research from 2000-2025. This study tries to identify fundamentally and thoroughly which journal-related multivariate analysis developments are the most significant, the most influential researchers, what research topics are developing, the types of datasets and methods used in multivariate analysis. Bibliometric analysis is used to look systematically based on relevant information from scientific publications, multivariate analysis articles accessed through IEEE Explore and Scopus. In the bibliometric analysis stage using Vosviewer, as many as 483 journals were analyzed for the number of citations, analysis of interconnected keywords and others. Therefore, it was found that the most significant journals were IEEE Transactions on Biomedical Engineering, IEEE Transactions on Geoscience and Remote Sensing. The most influential authors are B. Aiazzi; S. Baronti; M. Selva; with a total of 643 citations writing about the regression multivariate adopted to improve spectral quality. Emerging Research Topics Multivariate Time Series Forecasting. Methods that often appear are Regression, Correlations, Clustering, Principle Component Analysis.
References
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A-review-of-multivariate-analysis-2u9l6a05k2. (n.d.).
Arksey, H., & O'Malley, L. (2005). Scoping studies: Towards a methodological framework. International Journal of Social Research Methodology, 8(1), 19–32. https://doi.org/10.1080/1364557032000119616
Cappers, B. C. M., & Van Wijk, J. J. (2018). Exploring Multivariate Event Sequences Using Rules, Aggregations, and Selections. IEEE Transactions on Visualization and Computer Graphics, 24(1), 532–541. https://doi.org/10.1109/TVCG.2017.2745278
Chen, Z., & Wang, H. (2022). Research on Audit Simulation of Accounting Computerization Based on Internet Complex Discrete Dynamic Modeling Technology. Mathematical Problems in Engineering, 2022, 1–9. https://doi.org/10.1155/2022/5854105
Crino, S., & Brown, D. E. (2007). Global Optimization With Multivariate Adaptive Regression Splines. IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 37(2), 333–340. https://doi.org/10.1109/TSMCB.2006.883430
Deepa, N., Khan, M. Z., Prabadevi, B., Vincent P. M., D. R., Maddikunta, P. K. R., & Gadekallu, T. R. (2020). Multiclass Model for Agriculture Development Using Multivariate Statistical Method. IEEE Access, 8, 183749–183758. https://doi.org/10.1109/ACCESS.2020.3028595
Dell'Acqua, P., Bellotti, F., Berta, R., & De Gloria, A. (2015). Time-Aware Multivariate Nearest Neighbor Regression Methods for Traffic Flow Prediction. IEEE Transactions on Intelligent Transportation Systems, 16(6), 3393–3402. https://doi.org/10.1109/TITS.2015.2453116
Diansheng Guo. (2009). Flow Mapping and Multivariate Visualization of Large Spatial Interaction Data. IEEE Transactions on Visualization and Computer Graphics, 15(6), 1041–1048. https://doi.org/10.1109/TVCG.2009.143
Ding, J., Liu, H., Yang, L. T., Yao, T., & Zuo, W. (2020). Multiuser Multivariate Multiorder Markov-Based Multimodal User Mobility Pattern Prediction. IEEE Internet of Things Journal, 7(5), 4519–4531. https://doi.org/10.1109/JIOT.2019.2951134
Durichen, R., Pimentel, M. A. F., Clifton, L., Schweikard, A., & Clifton, D. A. (2015). Multitask Gaussian Processes for Multivariate Physiological Time-Series Analysis. IEEE Transactions on Biomedical Engineering, 62(1), 314–322. https://doi.org/10.1109/TBME.2014.2351376
Ermolova, N. Y., & Tirkkonen, O. (2012). Multivariate η-μ Fading Distribution with Constant Correlation Model. IEEE Communications Letters, 16(4), 454–457. https://doi.org/10.1109/LCOMM.2012.020712.112160
Fujiwara, T., Shilpika, Sakamoto, N., Nonaka, J., Yamamoto, K., & Ma, K.-L. (2021). A Visual Analytics Framework for Reviewing Multivariate Time-Series Data with Dimensionality Reduction. IEEE Transactions on Visualization and Computer Graphics, 27(2), 1601–1611. https://doi.org/10.1109/TVCG.2020.3028889
Georga, E. I., Protopappas, V. C., Ardigo, D., Marina, M., Zavaroni, I., Polyzos, D., & Fotiadis, D. I. (2013). Multivariate Prediction of Subcutaneous Glucose Concentration in Type 1 Diabetes Patients Based on Support Vector Regression. IEEE Journal of Biomedical and Health Informatics, 17(1), 71–81. https://doi.org/10.1109/TITB.2012.2219876
He, H., & Tan, Y. (2020). Unsupervised Classification of Multivariate Time Series Using VPCA and Fuzzy Clustering With Spatial Weighted Matrix Distance. IEEE Transactions on Cybernetics, 50(3), 1096–1105. https://doi.org/10.1109/TCYB.2018.2883388
James, F. C., & McCulloch, C. E. (1990). Multivariate Analysis in Ecology and Systematics: Panacea or Pandora's Box? Annual Review of Ecology and Systematics, 21(1), 129–166. https://doi.org/10.1146/annurev.es.21.110190.001021
Javed, K., Gouriveau, R., & Zerhouni, N. (2015). A New Multivariate Approach for Prognostics Based on Extreme Learning Machine and Fuzzy Clustering. IEEE Transactions on Cybernetics, 45(12), 2626–2639. https://doi.org/10.1109/TCYB.2014.2378056
Karadayi, Y., Aydin, M. N., & Ogrenci, A. S. (2020). Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Data Using Deep Learning: Early Detection of COVID-19 Outbreak in Italy. IEEE Access, 8, 164155–164177. https://doi.org/10.1109/ACCESS.2020.3022366
Karami, J., Alimohammadi, A., & Modabberi, S. (2012a). Analysis of the Spatio-Temporal Patterns of Water Pollution and Source Contribution Using the MODIS Sensor Products and Multivariate Statistical Techniques. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5(4), 1243–1255. https://doi.org/10.1109/JSTARS.2012.2187273
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Published
2025-05-10
How to Cite
Zuhra, F. (2025). Systematic Literature Review on Multivariate Analysis: Research Trends, Methods and Frameworks. Jurnal Saintifik (Multi Science Journal), 23(2), 109-126. https://doi.org/10.58222/js.v23i2.424
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