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首页> 外文期刊>Open Access Library Journal >Additive Decomposition with Arima Model Forecasts When the Trend Component Is Quadratic
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Additive Decomposition with Arima Model Forecasts When the Trend Component Is Quadratic

机译：当趋势分量是二次的时，与Arima模型预测的添加剂分解

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This paper demonstrates the use of Buys-Ballot table for identification of decomposition model using graphical method, when the trend cycle component is quadratic. A suitable ARIMA model was fitted, and was used for forecasting. Using the Buys-Ballot techniques, the column means, variances and standard deviation were estimated for the model identification. The additive model had no seasonal effect but, the multiplicative model had seasonal effect. The result of the illustrative example using the data of Nigeria Spot component price of oil (US Dollar per Barrel) showed the additive model to be the appropriate model for decomposition of this series. AR(2) model was identified as a suitable ARIMA model for the de-trended Nigeria Spot component price of oil. This was used to make forecast for the next twelve months. The obtained expected oil prices were compared with the observed prices. The comparison of expected and observed prices showed no significance difference between them, using Mean Absolute Percentage Error (MAPE).
机译：本文演示了使用Buys-Ballot表，用于使用图形方法识别分解模型，当趋势周期分量是二次的。合适的Arima模型安装，并用于预测。使用BUYS-BALPOT技术，估计柱装置，差异和标准偏差用于模型识别。添加剂模型没有季节性效果，但乘法模型具有季节性效果。说明性示例的结果，使用尼日利亚石油斑点成分价格（每桶美元）的数据显示添加剂模型是该系列分解的适当模型。 AR（2）模型被识别为可用于培训尼日利亚现场成分价格的合适的Arima模型。这用于对未来12个月进行预测。获得的预期油价与观察到的价格进行了比较。预期和观测价格的比较在使用平均绝对百分比误差（MAPE）之间没有显着差异。

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