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

Study on Electricity Demand Forecasting Techniques and a Selection Strategy| Chapter 2 | Current Aspects in Business, Economics and Finance Vol. 6

 Based on thorough empirical studies, a study of plants of well-known electrical demand predicting approaches is presented in this place study. In addition, a selection planning for the approach of projecting power demand has been presented. The plan was grown with recommendation from experts in energetic demand forecasts and the World Bank's eight-factor model (through a questionnaire). On the support of time skyline, accuracy, complicatedness, skill level, dossier volumes, geographic inclusion, adaptability, and cost, the methods have been judged. The most widely used technique, in accordance with the experts, is ARIMA (Autoregressive Integrated Moving Average) accompanying exponential smoothing and Kalman cleaning. Artificial Neural Networks with preprocessed Linear and Fuzzy inputs are the second most favorite approach. Support Vector Regression, which is currently being checked by many electrical engineers complicated in electricity demand forecasts, concede possibility now replace this approach. In addition to these patterns that are highlighted, this study still provides ratings for alternative methods based on the World Bank's eight-determinant model.  This research gives taxonomy of important power demand forecasting methods that will be advantageous for practitioners, academics, and juniors. The report also contains expert opinions on the hierarchy of eight selection tests based on a technique choice policy paper from the World Bank.

Author(s) Details:

Paraschos Maniatis,
Department of Business Administration, School of Business, Athens University of Economics and Business, Patision 76, GR - 104 34, Athens, Greece.

Please see the link here: https://stm.bookpi.org/CABEF-V6/article/view/8736