Comparison of Strategies for Multi-step-Ahead Prediction of Time Series Using Neural Network
Comparison of Strategies for Multi-step-Ahead Prediction of Time Series Using Neural Network
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DOI：
10.1109/acomp.2015.24
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发表时间：
2015-11
期刊：
影响因子：
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通讯作者：
Nguyen Hoang An;D. T. Anh
中科院分区：
文献类型：
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作者：
Nguyen Hoang An;D. T. Anh
如果时间序列的一步预测已经是一项具有挑战性的任务，那么执行多步预测就更加困难。在文献中已经提出了处理这个复杂问题的几种方法：递归（或迭代）策略，直接策略，递归和直接策略的组合，称为DirREC，多输入多输出（MIMO）策略，以及最后一种策略，称为DirMO，旨在保留直接和MIMO策略的优点。本文的目的是审查现有的战略，多步提前预测使用神经网络和经验比较他们的表现。为了实现这一目标，我们在三个数据集上进行了几次不同策略的实验：NN3竞争数据集，越南综合股价指数（VNINDEX）和FPT股票的收盘价。最一致的发现是，DirREC策略是优于所有其他策略的多步预测使用神经网络。
If the one-step forecasting of a time series is already a challenging task, performing multi-step ahead forecasting is more difficult. Several approaches that deal with this complex problem have been proposed in literature: recursive (or iterated) strategy, direct strategy, combination of both the recursive and direct strategies, called DirREC, the Multi-Input Multi-Output (MIMO) strategy, and the last strategy, called DirMO which aims to preserve the advantageous aspects of both the Direct and MIMO strategies. This paper aims to review existing strategies for multi-step ahead forecasting using neural networks and compare their performances empirically. To attain such an objective, we performed several experiments of these different strategies on three datasets: NN3 competition dataset, the Vietnam composite stock price index (VNINDEX) and the closing prices of the FPT stock. The most consistent findings are that the DirREC strategy is better than all the other strategies for multi-step ahead forecasting using neural network.