Jilin Institute of Chemical Technology, Longtan, Jilin, Jilin, China.
Changchun Institute of Technology, Chaoyang, Changchun, Jilin, China.
PLoS One. 2024 Sep 13;19(9):e0303990. doi: 10.1371/journal.pone.0303990. eCollection 2024.
Time series, a type of data that measures how things change over time, remains challenging to predict. In order to improve the accuracy of time series prediction, a deep learning model CL-Informer is proposed. In the Informer model, an embedding layer based on continuous wavelet transform is added so that the model can capture the characteristics of multi-scale data, and the LSTM layer is used to capture the data dependency further and process the redundant information in continuous wavelet transform. To demonstrate the reliability of the proposed CL-Informer model, it is compared with mainstream forecasting models such as Informer, Informer+, and Reformer on five datasets. Experimental results demonstrate that the CL-Informer model achieves an average reduction of 30.64% in MSE across various univariate prediction horizons and a reduction of 10.70% in MSE across different multivariate prediction horizons, thereby improving the accuracy of Informer in long sequence prediction and enhancing the model's precision.
时间序列是一种衡量事物随时间变化的数据集，它的预测仍然具有挑战性。为了提高时间序列预测的准确性，提出了一种深度学习模型 CL-Informer。在 Informer 模型中，添加了基于连续小波变换的嵌入层，使模型能够捕捉多尺度数据的特征，并且使用 LSTM 层进一步捕捉数据的依赖性，并处理连续小波变换中的冗余信息。为了验证所提出的 CL-Informer 模型的可靠性，将其与 Informer、Informer+和 Reformer 等主流预测模型在五个数据集上进行了比较。实验结果表明，CL-Informer 模型在各种单变量预测时间范围内平均降低了 30.64%的均方误差，在不同的多变量预测时间范围内降低了 10.70%的均方误差，从而提高了 Inforer 在长序列预测中的准确性，并增强了模型的精度。
