{Reference Type}: Journal Article
{Title}: Analysis and Forecasting of Area Under Cultivation of Rice in India: Univariate Time Series Approach.
{Author}: Annamalai N;Johnson A;
{Journal}: SN Comput Sci
{Volume}: 4
{Issue}: 2
{Year}: 2023
暂无{DOI}: 10.1007/s42979-022-01604-0
{Abstract}: 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.