Forecasting models for interval-valued time series
Forecasting models for interval-valued time series
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DOI：
10.1016/j.neucom.2008.02.022
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发表时间：
2008-10
期刊：
影响因子：
6
通讯作者：
A. L. S. Maia;F. D. Carvalho;Teresa B Ludermir
中科院分区：
文献类型：
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作者：
A. L. S. Maia;F. D. Carvalho;Teresa B Ludermir
本文介绍了区间值时间序列预测的方法。第一种和第二种方法分别基于自回归（AR）和自回归积分移动平均（ARIMA）模型。第三种方法是基于人工神经网络（ANN）模型，最后是基于一个混合的方法，结合ARIMA和ANN模型。每种方法分别拟合学习集中区间值时间序列所假设的区间值的中点和范围上的两个模型。时间序列的区间值的上下界的预测是通过从区间值的中点和范围的预测的组合来完成的。所提出的模型的评价是基于估计的平均行为的平均绝对误差和均方误差的框架内的蒙特卡罗实验。结果表明，该方法是有用的预测方案的区间值时间序列，并表明混合模型是一种有效的方法来提高预测精度的任何一个模型分别实现。
This paper presents approaches to interval-valued time series forecasting. The first and second approaches are based on the autoregressive (AR) and autoregressive integrated moving average (ARIMA) models, respectively. The third approach is based on an artificial neural network (ANN) model and the last is based on a hybrid methodology that combines both ARIMA and ANN models. Each approach fits, respectively, two models on the mid-point and range of the interval values assumed by the interval-valued time series in the learning set. The forecasting of the lower and upper bounds of the interval value of the time series is accomplished through a combination of forecasts from the mid-point and range of the interval values. The evaluation of the models presented is based on the estimation of the average behavior of the mean absolute error and mean squared error in the framework of a Monte Carlo experiment. The results demonstrate that the approaches are useful in forecasting alternatives for interval-valued time series and indicate that the hybrid model is an effective way to improve the forecasting accuracy achieved by any one of the models separately.