Analysis and Forecasting of Area Under Cultivation of Rice in India: Univariate Time Series Approach

Title

Analysis and Forecasting of Area Under Cultivation of Rice in India: Univariate Time Series Approach

Creator

Annamalai N.; Johnson A.

Description

This study uses three distinct models to analyse a univariate time series of data: Holt's exponential smoothing model, the autoregressive integrated moving average (ARIMA) model, and the neural network autoregression (NNAR) model. The effectiveness of each model is assessed using in-sample forecasts and accuracy metrics, including mean absolute percentage error, mean absolute square error, and root mean square log error. The area under cultivation in India for the following 5years is predicted using the model whose fitted values are most like the observed values. This is determined by performing a residual analysis. The time series data used for the study was initially found to be non-stationary. It is then transformed into stationary data using differencing before the models can be used for analysis and prediction. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

Source

SN Computer Science, Vol-4, No. 2

Date

2023-01-01

Publisher

Springer

Subject

ARIMA; Holt's exponential smoothing; NNAR models; Time series analysis; Univariate analysis

Coverage

Annamalai N., CHRIST (Deemed to be University), Pune Lavasa, India; Johnson A., CHRIST (Deemed to be University), Pune Lavasa, India

Rights

All Open Access; Bronze Open Access; Green Open Access

Relation

ISSN: 2662995X

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.1007/s42979-022-01604-0

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147423794&doi=10.1007%2fs42979-022-01604-0&partnerID=40&md5=5a2680691ea43880efa497ec4eaee860