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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.
摘要：
本研究使用三个不同的模型来分析单变量时间序列数据：霍尔特指数平滑模型，自回归移动平均积分(ARIMA)模型，和神经网络自回归(NNAR)模型。使用样本内预测和准确性指标评估每个模型的有效性，包括平均绝对百分比误差，平均绝对平方误差，和均方根对数误差。使用拟合值与观测值最相似的模型预测印度接下来5年的种植面积。这是通过执行残差分析来确定的。最初发现用于研究的时间序列数据是非平稳的。然后，在模型可用于分析和预测之前，使用差分将其转换为平稳数据。
