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首页> 外文期刊>American journal of applied sciences >Seasonal Time Series Data Forecasting by Using Neural Networks Multiscale Autoregressive Model | Science Publications
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Seasonal Time Series Data Forecasting by Using Neural Networks Multiscale Autoregressive Model | Science Publications

机译：神经网络的多尺度自回归模型预测季节性数据科学出版物

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> Problem statement: The aim of this research was to study further some latest progress of wavelet transform for time series forecasting, particularly about Neural Networks Multiscale Autoregressive (NN-MAR). Approach: There were three main issues that be considered further in this research. The first was some properties of scale and wavelet coefficients from Maximal Overlap Discrete Wavelet Transform (MODWT) decomposition, particularly at seasonal time series data. The second focused on the development of model building procedures of NN-MAR based on the properties of scale and wavelet coefficients. Then, the third was empirical study about the implementation of the proposed procedure and comparison study about the forecast accuracy of NN-MAR to other forecasting models. Results: The results showed that MODWT at seasonal time series data also has seasonal pattern for scale coefficient, whereas the wavelet coefficients are stationer. The result of model building procedure development yielded a new proposed procedure of NN-MAR model for seasonal time series forecasting. In general, this procedure accommodated input lags of scale and wavelet coefficients and other additional seasonal lags. In addition, the result showed that the proposed procedure works well for determining the best NN-MAR model for seasonal time series forecasting. Conclusion: The comparison study of forecast accuracy showed that the NN-MAR model yields better forecast than MAR and ARIMA models.
机译： > 问题陈述：本研究的目的是进一步研究小波变换在时间序列预测中的一些最新进展，尤其是关于神经网络多尺度自回归（NN-MAR）。 方法：本研究中有三个主要问题需要进一步考虑。首先是最大重叠离散小波变换（MODWT）分解产生的尺度和小波系数的某些属性，尤其是在季节性时间序列数据中。第二部分着重于基于尺度和小波系数特性的NN-MAR模型构建程序的开发。然后，第三次是对拟议程序的执行情况进行的实证研究，以及对NN-MAR的预测准确性与其他预测模型的比较研究。 结果：结果表明，MODWT在季节性时间序列数据上也具有比例系数的季节性模式，而小波系数是固定的。模型构建程序开发的结果产生了新的拟议的NN-MAR模型程序，用于季节时间序列预测。通常，此过程可容纳比例和小波系数的输入滞后以及其他附加的季节性滞后。此外，结果表明，所提出的程序对于确定季节性季节序列预测的最佳NN-MAR模型效果很好。 结论：预测准确性的比较研究表明，NN-MAR模型比MAR和ARIMA模型产生更好的预测。

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