Data Quality-based Gradient Optimization for Recurrent Neural Networks.

Time series forecasting holds significant value in various application scenarios. However, existing forecasting methods primarily focus on optimizing model architecture while neglecting the substantial impact of data quality on model learning. In this study, we aim to enhance model performance by optimizing data utilization based on data quality and propose a Data Quality-based Gradient Optimization (DQGO) method to facilitate training of recurrent neural networks. Firstly, we define sample quality as the matching degree between samples and model, and suggest using the attention entropy to calculate the sample quality through an attention mechanism. Secondly, we optimize the model's gradient vector by giving different weights to samples with different quality. Through experiments conducted on six datasets, the results demonstrate that DQGO significantly improves LSTM's performance. In certain cases, it even surpasses the state-of-the-art models.