Authors : P. Preethi; S. A. Jyothi Rani; V. V. Haragopal

Volume/Issue : Volume 10 - 2025, Issue 7 - July

---

Google Scholar : https://tinyurl.com/2tujyt67

Scribd : https://tinyurl.com/497fhkne

DOI : https://doi.org/10.38124/ijisrt/25jul1699

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

---

Abstract : The Index of Industrial Production (IIP) is a key economic indicator that tracks manufacturing activity across various sectors. This paper aims to predict the IIP for three sub-series—Mining, Manufacturing, and Electricity—using both conventional statistical methods and deep learning approaches, analyzing data from April 2012 to September 2022. Model performance is evaluated by comparing Mean Square Error (MSE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE)1 . The results show that the RNN model outperforms other models for all three sub-series, and is used to forecast these sub-series from October 2022 to September 2023.

Keywords : IIP, Mining, Manufacturing, Electricity, SARIMA, FFNN, RNN, LSTM, Forecasting.

References :

1. Patil, Preethi, S. A. Jyothirani, and V. V. Haragopal. "Impact of Lockdown on India's Index of Industrial Production–Traditional and Deep Learning Statistical Approach." European Journal of Mathematics and Statistics 3.4 (2022): 62-70.
2. Sodhi, ManMohan S., et al. "A robust and forward-Looking industrial production indicator." Economic and Political Weekly (2013): 126-130.
3. Rani, SA Jyothi, and N. Chandan Babu. "Forecasting production of rice in India–using Arima and deep learning methods." Int J Math Trends Technol (IJMTT) 66.4 (2020).
4. Singh, Salam Shantikumar, T. Loidang Devi, and T. Deb Roy. "Time series analysis of the index of industrial production of India." IOSR Journal of Mathematics 12.3 (2016): 1-7.