Exploring ARIMA Models with Interacted Lagged Variables for Forecasting

Title

Exploring ARIMA Models with Interacted Lagged Variables for Forecasting

Creator

Thangarajan B.; Nagaraja M.S.; Dhandra B.V.

Description

Including interactions among the explanatory variables in regression models is a common phenomenon. However, including interactions existing among lagged variables in autoregressive models has not been explored so far. In this paper, Autoregressive Integrated Moving Average (ARIMA) model with interactions among the lagged variables is proposed for improving forecast accuracy. The methodology for identifying the interacted lagged variables and including them in the ARIMA model is suggested. Using five different data sets of different types, the paper explores the effect of interacted lagged variables in ARIMA model. The experimental results exhibit that when interactions do actually exist, ARIMA model with interactions improves the forecast accuracy as compared to ARIMA model without interactions. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Source

Trends in Mathematics, Vol-Part F2357, pp. 735-745.

Date

2024-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Autoregressive Integrated Moving Average model; Interactions among lagged variables

Coverage

Thangarajan B., CHRIST (Deemed to be University), Bengaluru, India; Nagaraja M.S., CHRIST (Deemed to be University), Bengaluru, India; Dhandra B.V., CHRIST (Deemed to be University), Bengaluru, India

Rights

Restricted Access

Relation

ISSN: 22970215

Format

Online

Language

English

Type

Book chapter

Identifier

https://doi.org/10.1007/978-3-031-41420-6_61

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187166729&doi=10.1007%2f978-3-031-41420-6_61&partnerID=40&md5=1c3de07337533d2b8499ed788c764205