论文标题
基于自动编码器小波的深神经网络，具有注意植物生长的注意力机制
An autoencoder wavelet based deep neural network with attention mechanism for multistep prediction of plant growth
论文作者
论文摘要
在许多现实生活问题中，多步预测对于时间序列分析具有重要意义。现有方法主要集中于一步预测，因为多个步骤预测通常由于预测错误的积累而失败。本文提出了一种预测农业植物生长的新方法，重点是预测植物茎直径变化（SDV）。提出的方法包括三个主要步骤。首先，将小波分解应用于原始数据，以促进模型拟合并降低其中的噪声。然后，使用长期内存（LSTM）开发编码器框架框架，并用于从数据中提取适当的特征。最后，提出了一个复发性神经网络，包括LSTM和注意机制，用于在时间序列数据中建模长期依赖性。提出了实验结果，以说明提出的方法的良好性能，并且在诸如RMSE，MAE和MAPE等误差标准方面，它显着优于现有模型。
Multi-step prediction is considered of major significance for time series analysis in many real life problems. Existing methods mainly focus on one-step-ahead forecasting, since multiple step forecasting generally fails due to accumulation of prediction errors. This paper presents a novel approach for predicting plant growth in agriculture, focusing on prediction of plant Stem Diameter Variations (SDV). The proposed approach consists of three main steps. At first, wavelet decomposition is applied to the original data, as to facilitate model fitting and reduce noise in them. Then an encoder-decoder framework is developed using Long Short Term Memory (LSTM) and used for appropriate feature extraction from the data. Finally, a recurrent neural network including LSTM and an attention mechanism is proposed for modelling long-term dependencies in the time series data. Experimental results are presented which illustrate the good performance of the proposed approach and that it significantly outperforms the existing models, in terms of error criteria such as RMSE, MAE and MAPE.
