A Univariate and Multivariate Time Series Analysis for the Prediction of Maize Production in India

Title

A Univariate and Multivariate Time Series Analysis for the Prediction of Maize Production in India

Creator

Johnson, Amala; John, Nimitha; Paulose, Joy

Description

This study examines the use of time series analysis for predicting maize production in India. The objective is to analyze the relationship between maize productions, domestic consumption, exports, and to forecast maize production using various time series models. The study employs cointegration techniques such as Johansen's test, Engle-Granger test, and Granger causality test to determine the long-term relationship between the variables. The findings exhibit that there is a bidirectional causal relationship between domestic consumption and export series and between domestic consumption and production series and that all three variables are co-integrated. To forecast maize production, the study employs both multivariate and univariate time series models. The multivariate models used are vector auto regressive and vector error correction models, while the univariate models used are ARIMA (auto regressive integrated moving averages), Holts exponential smoothing, NNAR (neural network auto regression), K-nearest neighbors (KNN), and LSTM (long short-term memory). The best forecast model is selected on the basis of a comparison of three evaluation metrics: mean absolute square error, mean absolute percentage error, and root mean square log error. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

Source

Lecture Notes in Networks and Systems;Volume;1293;pp.397-411

Date

01-01-2025

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

ARIMA; Co-integration; LSTM; NNAR; VECM

Coverage

Johnson A., CHRIST (Deemed to be University), Bengaluru, India; John N., CHRIST (Deemed to be University), Bengaluru, India; Paulose J., CHRIST (Deemed to be University), Bengaluru, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISSN: 23673370; ISBN: 978-981963246-6;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-96-3247-3_30

https://www.scopus.com/pages/publications/105006921095?origin=resultslist