Modeling Consumer Price Index: A Machine Learning Approach

Title

Modeling Consumer Price Index: A Machine Learning Approach

Creator

Sarangi P.K.; Sahoo A.K.; Sinha S.

Description

The change in price of a group of goods and services is reflected in terms of consumer price index (CPI), making it one of the most important economic indicators. This is also the mostly used measure of inflation. Forecasted CPI values help the Government to take corrective measures to control the economic conditions of the country. This paper implements and examines two machine learning models such as artificial neural network (ANN) and ANN model optimized with particle swarm optimization (PSO) known as ANN-PSO to assess the accuracy in predictability of CPI. The data set for four groups such as food and beverages, housing, clothing, and footwear used for the calculation of all India CPI has been taken from the official website of the Government of India. The mean absolute percentage error (MAPE) has been used as the validator for model accuracy. The MAPE calculated for all experiments are less than 10% which indicates that the ANN-PSO models used are highly accurate for prediction of CPI of India. 2022 Wiley-VCH GmbH

Source

Macromolecular Symposia, Vol-401, No. 1

Date

2022-01-01

Publisher

John Wiley and Sons Inc

Subject

artificial neural network; consumer price index; machine learning; mean absolute percentage error; particle swarm optimization

Coverage

Sarangi P.K., Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, 140401, India; Sahoo A.K., Graphic Era Hill University, Dehradun, 248002, India; Sinha S., Christ (Deemed to be University), Delhi, 201003, India

Rights

Restricted Access

Relation

ISSN: 10221360; CODEN: MSYME

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.1002/masy.202100349

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125152652&doi=10.1002%2fmasy.202100349&partnerID=40&md5=5f4d28551421c3b064c3388be2441d61