Efficacy of Artificial Neural Networks (ANN) as a Tool for Predictive Analytics

Title

Efficacy of Artificial Neural Networks (ANN) as a Tool for Predictive Analytics

Creator

Sinha D.; Sarangi P.K.; Sinha S.

Description

Predictive analytics could also be defined as the application of statistical techniques and mathematical modeling to anticipate the future performance and expected return on investments. Predictive analytics examines the most recent and the historical data to see if the same pattern is likely to reoccur or not. This gives an opportunity to businessmen and financial investors to make an appropriate decision about their investments and expected returns. Ever since the development of ANN technique, researchers have tried to create a number of predictive models using ANN. The chapter is focused on defining predictive analytics and the tools used in predictive analytics, with a special orientation on Artificial Neural Networks. The objective of the chapter is to establish ANN as an effective technique for making appropriate predictions and thereby contributing toward the decision-making in various spheres using the outcomes from various researches. The chapter also aims to explain the step-by-step process of ANN in outcome prediction with the help of example. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

Source

Analytics Enabled Decision Making, pp. 123-138.

Date

2023-01-01

Publisher

Springer Nature

Subject

Artificial Neural Network (ANN); Decision-making; Forecasting; Output prediction; Predictive analytics

Coverage

Sinha D., CHRIST (Deemed to be University), Delhi NCR, India; Sarangi P.K., Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India; Sinha S., CHRIST (Deemed to be University), Delhi NCR, India

Rights

Restricted Access

Relation

ISBN: 978-981199658-0; 978-981199657-3

Format

Online

Language

English

Type

Book chapter

Identifier

https://doi.org/10.1007/978-981-19-9658-0_6

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171511956&doi=10.1007%2f978-981-19-9658-0_6&partnerID=40&md5=d1fa562df36d8892ada5af694bd85888