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An improved Elman neural network with piecewise weighted gradient for time series prediction

机译：具有分段加权梯度的改进Elman神经网络用于时间序列预测

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摘要

Time series prediction is an important tool for system analysis. Traditional forecasting methods usually achieve prediction by establishing static model or only mining the information from the sequence itself, but without considering the dynamics of the system or its triggering factors. Elman neural network (ENN) is a dynamical model that can remember historical states. In order to better establish the real multivariate time series model and improve the single-step prediction accuracy, this study proposes an improved regularized ENN method based on piecewise time weighted gradient (PWRENN). Each time the parameters of PWRENN model are updated, the weighted gradient is calculated by weighing the current gradient and the previous gradients according to a monotonically increasing temporal function. By considering both the current gradient and previous gradients, PWRENN is able to simulate the time-lag effect in the time series prediction problem. Moreover, the system environment may change with time, and the historical data of the same slot at a different time should have different effects on the predicted data. This work uses a piecewise time function with different parameters at different time periods to more accurately model the real system. In PWRENN, L2 regularization is adopted to solve the overfitting problem and enhance the generalization performance. Furthermore, the effectiveness of the proposed method is verified in an artificial Mackey-Glass time series prediction and three landslide displacement predictions. (C) 2019 Elsevier B.V. All rights reserved.
机译：时间序列预测是系统分析的重要工具。传统的预测方法通常通过建立静态模型或仅从序列本身中挖掘信息来实现预测，而没有考虑系统或其触发因素的动态。 Elman神经网络（ENN）是可以记住历史状态的动力学模型。为了更好地建立真实的多元时间序列模型并提高单步预测精度，本研究提出了一种基于分段时间加权梯度（PWRENN）的改进的正则化ENN方法。每次更新PWRENN模型的参数时，将根据单调递增的时间函数对当前梯度和先前的梯度进行加权，从而计算加权梯度。通过同时考虑当前梯度和先前梯度，PWRENN能够模拟时间序列预测问题中的时滞效应。此外，系统环境可能会随时间变化，并且同一插槽在不同时间的历史数据对预测数据的影响应有所不同。这项工作在不同的时间段使用具有不同参数的分段时间函数来更准确地对实际系统建模。在PWRENN中，采用L2正则化来解决过拟合问题并增强泛化性能。此外，在人工Mackey-Glass时间序列预测和三个滑坡位移预测中验证了该方法的有效性。 （C）2019 Elsevier B.V.保留所有权利。

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  • 来源
    《Neurocomputing》 |2019年第24期|199-208|共10页
  • 作者

    Zhang Ying; Wang Xiaoping; Tang Huiming;

  • 作者单位

    Huazhong Univ Sci & Technol, Sch Artificial Intelligence & Automat, Wuhan 430074, Hubei, Peoples R China|Minist China, Key Lab Image Proc & Intelligent Control Educ, Wuhan 430074, Hubei, Peoples R China;

    Huazhong Univ Sci & Technol, Sch Artificial Intelligence & Automat, Wuhan 430074, Hubei, Peoples R China|Minist China, Key Lab Image Proc & Intelligent Control Educ, Wuhan 430074, Hubei, Peoples R China;

    China Univ Geosci, Fac Engn, Wuhan 430074, Hubei, Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Time series prediction; Elman neural network; Piecewise time weighted gradient; Regularization method;

    机译：时间序列预测;Elman神经网络;分段时间加权梯度;正规化方法;

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