A deep implicit memory Gaussian network for time series forecasting

In recent years, significant achievements have been made in time series forecasting using deep learning methods, particularly the Long Short-Term Memory Network (LSTM). However, time series often exhibit complex patterns and relationships like trends, seasonal patterns and irregularities, and LSTM networks fail to effectively strengthen long-term dependence in time series data, which may lead to inaccurate forecasting effect or performance degradation. Therefore, building a model to explore the temporal dependence within the time series data completely still remains a challenge. In this paper, we propose a novel Deep Implicit Memory Gaussian (DIMG) Network based on bidirectional deep memory kernel process and the implicit features enhancement method for time series forecasting. We first use the implicit features enhancement method to obtain the hidden features of the data according to the nonlinear mapping characteristics of encoder. Then, a new deep learning process called bidirectional deep memory kernel has been developed, which merges the structural properties of deep learning with the adaptability of kernel methods to capture intricate information and memory structures in sequential data. This process fully encapsulates the structure of Bi-LSTM and Gaussian process regression (GPR). Finally, the performance of the proposed model is evaluated on two real-world datasets. The experimental results verify that our model outperforms other reported methods in terms of prediction accuracy.

Automated summary

In this paper, a novel Deep Implicit Memory Gaussian (DIMG) Network is proposed for time series forecasting, which combines bidirectional deep memory kernel process and implicit features enhancement method. The model is able to capture complex patterns in time series data and improve prediction accuracy by integrating the structural properties of deep learning with the adaptability of kernel methods.