An Evolving Autoregressive Predictor for Time Series Forecasting

De Z. Li; Wilson Wang; Fathy Ismail

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An Evolving Autoregressive Predictor for Time Series Forecasting

机译：不断发展的时间序列预测的自回归预测器

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Autoregressive (AR) model is a common predictor that has been extensively used for time series forecasting. Many training methods can used to update AR model parameters, for instance, least square estimate and maximum likelihood estimate; however, both techniques are sensitive to noisy samples and outliers. To deal with the problems, an evolving AR predictor, EAR, is developed in this study to enhance prediction accuracy and mitigate the effect of noisy samples and outliers. The model parameters of EAR are trained with an Adaptive Least Square Estimate (ALSE) method, which can learn samples characteristics more effectively. In each training epoch, the ALSE weights the samples by their fitting accuracy. The samples with larger fitting errors will be given a larger penalty value in the cost function; however the penalties of difficult-to-predict samples will be adaptively reduced to enhance the prediction accuracy. The effectiveness of the developed EAR predictor is verified by simulation tests. Test results show that the proposed EAR predictor can capture the dynamics of the time series effectively and predict the future trend accurately.

机译：自回归（AR）模型是一种通用的预测器，已广泛用于时间序列预测。许多训练方法可以用来更新AR模型参数，例如最小二乘估计和最大似然估计；但是，这两种技术都对嘈杂的样本和异常值敏感。为了解决这些问题，在这项研究中开发了一个不断发展的AR预测器EAR，以提高预测精度并减轻噪声样本和异常值的影响。 EAR的模型参数通过自适应最小二乘估计（ALSE）方法进行训练，该方法可以更有效地学习样本特征。在每个训练时期，ALSE都通过拟合精度对样本进行加权。拟合误差较大的样本将在成本函数中获得较大的惩罚值；然而，难以预测的样本的惩罚将被自适应地降低以提高预测精度。通过仿真测试验证了开发的EAR预测器的有效性。测试结果表明，所提出的EAR预测器可以有效地捕获时间序列的动态并准确预测未来趋势。

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来源
《American journal of engineering and applied sciences》 |2015年第1期|57-62|共6页
作者
De Z. Li; Wilson Wang; Fathy Ismail;
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作者单位

Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, Canada,Department of Mechanical Engineering, Lakehead University, Thunder Bay, Canada;

Department of Mechanical Engineering, Lakehead University, Thunder Bay, Canada;

Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, Canada;

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正文语种 eng
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关键词
Autoregressive Model; Boosting; Adaptive Least Square Estimate; Time Series Forecasting;

机译：自回归模型助推;自适应最小二乘估计;时间序列预测;

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专利

1. An Evolving Autoregressive Predictor for Time Series Forecasting | Science Publications [J] . De Z. Li, Fathy Ismail, Wilson Wang American journal of engineering and applied sciences . 2015,第1期

机译：用于时间序列预测的不断发展的自回归预测器|科学出版物
2. Comparison of Autoregressive (AR), Vector Autoregressive (VAR), Space Time Autoregressive (STAR), and Generalized Space Time Autoregressive (GSTAR) in Forecasting (Case: Simulation study with Autoregressivepattern) [J] . Yixian Yang, Caixue Zhou International Journal of Horticulture & Crop Science Research . 2018,第2期

机译：自回归（AR），矢量自动增加（VAR），空间时间自回归（明星）以及预测中的广义空间时间自回归（GSTAR）的比较（案例：用AutoregressPattern进行仿真研究）
3. An improved Space-Time Autoregressive Moving Average (STARMA) model for Modelling and Forecasting of Spatio-Temporal time-series data [J] . Santosha Rathod, Bishal Gurung, K.N. Singh, Journal of the Indian Society of Agricultural Statistics . 2018,第3期

机译：一种改进的时空自回归移动平均（Starma）模型，用于建模和预测时空时间序列数据
4. EVOLVING AUTOREGRESSIVE NEURAL NETWORKS FOR THE PROBLEM OF MODELING NONLINEAR HETEROGENEOUS TIME SERIES [C] . NIMA REYHANI, MAHMOOD KHARRAT International FLINS(Fuzzy Logic and Intelligent Technologies in Nuclear Science) Conference; 20040901-03; Blankenberge(BE) . 2004

机译：进化的自回归神经网络对非线性异质时间序列的建模问题
5. Estimation and forecasting of air handling condensate recovery using multiple regression analysis and time-series autoregressive models [D] . Kajale, Atul 2013

机译：使用多元回归分析和时间序列自回归模型估算和预测空气处理冷凝物的回收率
6. Assessment of autoregressive integrated moving average (ARIMA), generalized linear autoregressive moving average (GLARMA), and random forest (RF) time series regression models for predicting influenza A virus frequency in swine in Ontario, Canada [O] . Tatiana Petukhova, Davor Ojkic, Beverly McEwen, 2012

机译：评估加拿大安大略省猪甲型流感病毒频率的自回归综合移动平均值（ARIMA），广义线性自回归移动平均值（GLARMA）和随机森林（RF）时间序列回归模型
7. An Evolving Autoregressive Predictor for Time Series Forecasting [O] . De Z. Li, Wilson Wang, Fathy Ismail 2015

机译：时间序列预测的不断发展的自回归预测因子
8. Evolving Cascade-Correlation Networks for Time-Series Forecasting [R] . McDonnell, J. R., Waagen, D. E. 1995

机译：用于时间序列预测的演进级联相关网络

An Evolving Autoregressive Predictor for Time Series Forecasting

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