Reducing Dimensions in Time Series Data using Remora Optimization Algorithm

Reducing Dimensions in Time Series Data using Remora Optimization Algorithm
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© 2022 by IJETT Journal
Volume-70 Issue-7
Year of Publication : 2022
Authors : M. P. Rekha, K. Perumal
DOI : 10.14445/22315381/IJETT-V70I7P212

How to Cite?

M. P. Rekha, K. Perumal, "Reducing Dimensions in Time Series Data using Remora Optimization Algorithm" International Journal of Engineering Trends and Technology, vol. 70, no. 7, pp. 108-121, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I7P212

Abstract
In this paper, develop a Hybrid Recurrent Neural Network Model (HRNN) with Principal Component Analysis (PCA) for efficient weather forecasting. Initially, the databases are collected from the open-source system. After that, PCA is utilized to reduce the dimension of the input collected data. The huge amount of data reduces the forecasting accuracy with the repeated values and attributes. Hence, PCA is utilized for reducing the similar dimension of attributes. The reduction data is utilized in the proposed weather forecasting model. The HRNN combines Recurrent Neural Network (RNN) and Remora Optimization Algorithm (ROA). The proposed HRNN is working with two phases: training and testing. The collected data is divided into two parts as80% and 20%. 80% of data is utilized for the training phase of the proposed technique. The remaining 20% is utilized for the testing phase of the proposed technique. The proposed technique is implemented, and performance metrices evaluate performance. It is compared with the conventional techniques such as Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO) to validate the proposed technique.

Keywords
Recurrent Neural Network, Remora Optimization Algorithm, Principal component analysis, Hybrid recurrent neural network model.

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