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Analysis and Forecasting of Area Under Cultivation of Rice in India: Univariate Time Series Approach by Niveditha Annamalai; Amala Johnson is a Computer Science article available to read on EtoBox.

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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 5 years 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. ## Keywords Time series analysis • Univariate analysis • ARIMA • Holt's exponential smoothing • NNAR models This article is part of the topical collection "Advances in Computational Intelligence for Artificial Intelligence, Machine Learning, Internet of Things and Data Analytics" guest edited by S. Meenakshi Sundaram, Young Lee and Gururaj K S.

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Author

Niveditha Annamalai; Amala Johnson

Publisher

Springer Science and Business Media LLC

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)