Application of Forecasting Model to Study the Population Growth of India | Chapter 4 | Mathematics and Computer Science: Research Updates Vol. 4

This study aims to compare the forecasting accuracy of three-time series models—Autoregressive Integrated Moving Average (ARIMA), Exponential Smoothing State Space Model (ETS), and State-Space Model with Kalman Filtering—for predicting India’s population trends from 2021 to 2091.

Study Design: A comparative analysis of time series forecasting models based on historical population data.

Methodology: The study employs statistical and machine learning forecasting models: ARIMA, ETS, and Kalman Filtering. These models were applied to obtain forecasts and compared based on their errors and alignment with currently available data.

Results: The ARIMA model estimates a decline in India's population after 2051, while the ETS and Kalman Filtering models suggest continuous growth until 2091. The ARIMA model shows the lowest error rate (MAE: 14.63, RMSE: 24.24, MAPE: 3.36%), providing better short-term accuracy. The ETS model offers a more reliable long-term projection, though with slightly higher errors. The Kalman Filtering model presents the highest error values (MAE: 42, RMSE: 54.06, MAPE: 12.67%), reflecting greater uncertainty in its estimates.

Conclusion: Among the statistical models, ARIMA delivers the most accurate short-term forecasts, while ETS and Kalman Filtering are more suitable for long-term projections. In the machine learning forecast, XGBoost was found to be the most accurate for long-term population forecasting. The study highlights the strengths and limitations of each model and underscores the importance of selecting an appropriate forecasting method based on the required time horizon and accuracy needs.

 

Author (s) Details

 

Abhishek Pandey
Department of Computer Science and Engineering, Institute of Advanced Research, The University for Innovation, Gandhinagar, Gujarat. Pin :382426, India.

 

Sanjay Sonar
Department of Computer Science and Engineering, Institute of Advanced Research, The University for Innovation, Gandhinagar, Gujarat. Pin :382426, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/mcsru/v4/4778

An Overview of Statistical Techniques for Analysis of Data in Agricultural Research | Chapter 11 | Emerging Issues in Agricultural Sciences Vol. 8

 The significance of statistical analysis is paramount in research, particularly in situations involving the collection, classification, analysis, and interpretation of numerical data. Statistical principles find widespread application in various types of experimental studies and serve as a critical component in agricultural research endeavors. The inherent variability present in commonly used experimental materials within agricultural research necessitates the utilization of statistical methods, leading to numerous advancements and innovations in the field of statistics. The selection of an appropriate tool for data analysis and subsequent processes involving statistical components has become a matter of concern. This chapter delves into the diverse statistical techniques essential for the analysis of agricultural data and the derivation of valid conclusions.

Author(s) Details:

Rahul Banerjee,
ICAR-Indian Agricultural Statistics Research Institute, Library Avenue, Pusa, New Delhi, India.

Bharti,
ICAR-Indian Agricultural Statistics Research Institute, Library Avenue, Pusa, New Delhi, India.

Pankaj Das,
ICAR-Indian Agricultural Statistics Research Institute, Library Avenue, Pusa, New Delhi, India.

Varun Srivastava,
Department of Computer Science and Information Technology, Jaypee Institute of Information Technology, Noida, India.

Ankita,
Department of Agricultural Statistic and Computer Application, Birsa Agricultural University, Kanke, Ranchi, India.

Suraj Kataria,
Department of Anthropology, University of Delhi, Delhi, India.

Bulbul Ahmed,
Department of Agriculture, Galgotias University, Greater Noida, India.

Nitin Varshney,
Department of Agricultural Statistics, Navsari Agricultural University, Navsari, India.

Please see the link here: https://stm.bookpi.org/EIAS-V8/article/view/12877