Comparative Analysis of CPI prediction for India using Statistical methods and Neural Networks

Title

Comparative Analysis of CPI prediction for India using Statistical methods and Neural Networks

Creator

Singh A.; Shukla B.; Jos J.

Description

Inflation is one of the main issues affecting the world economy right now, necessitating the accurate inflation prediction for the development of tools and policies by the monetary authorities to prevent extreme price volatility. Expectations of inflation influence many financial and economic actions, and this dependence motivates economists to develop techniques for precise inflation forecasting. Nearly everyone in the economy is impacted by inflation, including lending institutions, stock brokers, and corporate financial officials. In many cases, inflation determines whether a firm will accept a particular project or if banks will make a particular loan. These different economic actors can modify their financial portfolios, strategic goals, and upcoming investments if they are able to forecast changes in inflation rates. The multiple interaction economic components that depend on inflation will be better understood by economic agents operating in a business context if inflation forecasting accuracy is improved. There are numerous techniques to forecast inflation ranging from basic statistical methods to complex neural network methods. Therefore, this paper employs LSTM model to train and analyze the Consumer Price Index (CPI) indicators to obtain inflation-related prediction results. The experimental results on historical data show that the statistical model has good performance in predicting India's inflation rate compared to deep learning methods in case of smaller dataset. 2023 IEEE.

Source

2023 2nd International Conference for Innovation in Technology, INOCON 2023

Date

2023-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

ARIMA; Consumer Price Index; GRU; Holt-Winters; LSTM; RNN; SARIMA; Stacked LSTM

Coverage

Singh A., Christ (Deemed to Be University), Dept. of Data Science, Pune, India; Shukla B., Christ (Deemed to Be University), Dept. of Data Science, Pune, India; Jos J., Christ (Deemed to Be University), Dept. of Data Science, Pune, India

Rights

Restricted Access

Relation

ISBN: 979-835032092-3

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/INOCON57975.2023.10101073

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85158028795&doi=10.1109%2fINOCON57975.2023.10101073&partnerID=40&md5=af2acde42f28062b99ddbb6361718b71