Time Series Prediction Using DBN and ARIMA
Time Series Prediction Using DBN and ARIMA
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DOI：
10.1109/ccats.2015.15
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发表时间：
2015-08
期刊：
影响因子：
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通讯作者：
T. Hirata;T. Kuremoto;M. Obayashi;S. Mabu;Kunikazu Kobayashi
中科院分区：
文献类型：
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作者：
T. Hirata;T. Kuremoto;M. Obayashi;S. Mabu;Kunikazu Kobayashi
时间序列数据的分析与预测是研究非线性现象的重要手段。自上个世纪以来，对时间序列预测的研究已经有了很长的历史，线性模型如自回归积分移动平均（ARIMA）模型和非线性模型如多层感知机（MLP）模型都是众所周知的。作为目前最先进的方法，最近提出了一种使用多个受限玻尔兹曼机（rbm）的深度信念网络（DBN）。在本研究中，我们提出了一种新的预测方法，该方法不仅由一种DBN与RBM和MLP组成，而且还包括ARIMA。对实际数据的时间序列和混沌时间序列进行了预测实验，结果表明了所提方法的有效性。
Time series data analyze and prediction is very important to the study of nonlinear phenomenon. Studies of time series prediction have a long history since last century, linear models such as autoregressive integrated moving average (ARIMA) model, and nonlinear models such as multi-layer perceptron (MLP) are well-known. As the state-of-art method, a deep belief net (DBN) using multiple Restricted Boltzmann machines (RBMs) was proposed recently. In this study, we propose a novel prediction method which composes not only a kind of DBN with RBM and MLP but also ARIMA. Prediction experiments for the time series of the actual data and chaotic time series were performed, and results showed the effectiveness of the proposed method.