Application of Multilayer Perceptron for Forecasting of Selected IIPs of India – An Empirical Analysis - Das, Dipankar; Tripathi, Awanish Kumar; Shah, Ayushi; Mehta, Samarth (International Journal of Computer Sciences and Engineering, 2025)
The Index of Industrial Production (IIP) is an important indicator and a univariate time series data in nature. In the present study, the authors endeavored to develop forecasting models for twenty three (23) selected IIPs of India. The models were developed using Multilayer Perceptron. The study focused at (i) development of forecasting models, (ii) visualization of them, and (iii) analyzing the accuracies of the developed models. The study showed a mixed result with approximately twenty two percent (22%) i.e. five (5) out of twenty three (23) of the IIPs under study gave very good forecasting accuracy in terms of Mean Absolute Percentage Error (MAPE less than five), approximately twenty six percent (26%) i.e. six (6) out of twenty three (23) of the IIPs under study gave good forecasting accuracy (MAPE greater than or equal to five and MAPE less than ten) and approximately thirteen percent (13%) i.e. three (3) out of twenty three (23) of the IIPs under study gave moderate forecasting accuracy (MAPE greater than or equal to ten & MAPE less than twelve).