Exploring Population Dynamics in Nashik District: Applying Polynomial Extrapolation
DOI:
https://doi.org/10.53555/jaz.v44iS8.3469Keywords:
Polynomial Degree, Data Fitting Accuracy, Population Trends, Nashik District, OverfittingAbstract
In this study we have investigates the impact of polynomial degree selection on data fitting accuracy in analyzing population trends within Nashik District. Through a series of figures, it becomes evident that lower-degree polynomials, including linear and quadratic models, inadequately match the dataset's complexity. However, with escalating polynomial degrees, a notable improvement in fitting effectiveness emerges. This analysis highlights the critical role of selecting an appropriate polynomial degree in accurately representing underlying trends. While higher-degree polynomials offer improved fitting, the risk of overfitting, especially with smaller datasets, necessitates a delicate balance between complexity and accuracy. Understanding dataset characteristics is pivotal in determining the optimal polynomial degree for effective representation and prediction of population trends in Nashik District.
Downloads
References
Bahadori, A. (2011). Prediction of moisture content of natural gases using simple Arrhenius-type function. Central European Journal of Engineering, 1(1), 81–88.
Bahadori, A., & Nouri, A. (2012). Prediction of critical oil rate for bottom water coning in anisotropic and homogeneous formations. Journal of Petroleum Science and Engineering, 82, 125–129.
Census tables | Government of India. (n.d.). Retrieved January 6, 2024, from https://censusindia.gov.in/census.website/data/census-tables
Ghazali, R., Hussain, A., & El-Deredy, W. (2006). Application of Ridge Polynomial Neural Networks to Financial Time Series Prediction. The 2006 IEEE International Joint Conference on Neural Network Proceedings, 913–920. https://doi.org/10.1109/IJCNN.2006.246783
Harju, P. T. (1997). Polynomial prediction using incomplete data. IEEE Transactions on Signal Processing, 45(3), 768–770. https://doi.org/10.1109/78.558500
Hussain, A. J., Knowles, A., Lisboa, P. J. G., & El-Deredy, W. (2008). Financial time series prediction using polynomial pipelined neural networks. Expert Systems with Applications, 35(3), 1186–1199. https://doi.org/10.1016/j.eswa.2007.08.038
Lavanya, Y., & Achireddy, C. (2016). APPLICATIONS OF INTERPOLATION. 3(5), 5.
Ostertagová, E. (2012). Modelling using Polynomial Regression. Procedia Engineering, 48, 500–506. https://doi.org/10.1016/j.proeng.2012.09.545
Wu, W., Liu, J., Wang, H., Tang, F., & Xian, M. (2018). PPolyNets: Achieving High Prediction Accuracy and Efficiency With Parametric Polynomial Activations. IEEE Access, 6, 72814–72823. https://doi.org/10.1109/ACCESS.2018.2882407
Zaw, W. T., & Naing, T. T. (2009). Modeling of Rainfall Prediction over Myanmar Using Polynomial Regression. 2009 International Conference on Computer Engineering and Technology, 1, 316–320. https://doi.org/10.1109/ICCET.2009.157
Zjavka, L. (2015). Wind speed forecast correction models using polynomial neural networks. Renewable Energy, 83, 998–1006. https://doi.org/10.1016/j.renene.2015.04.054
Downloads
Published
Issue
Section
License
Copyright (c) 2024 S.S. Jadhav, R. O. Parmar, N. K. Patil, S.G. Pagare, M.A. Joshi, S.L. Khairnar
This work is licensed under a Creative Commons Attribution 4.0 International License.
