Deep Learning Approach for Time Series Forecasting

Dear Colleagues,

In recent years, deep learning (DL) methodologies have revolutionized the field of artificial intelligence (AI), particularly in the domain of time series forecasting. With their ability to capture complex nonlinear relationships in time-dependent data, these advanced models have shown remarkable success across various sectors, including finance, transportation, weather, energy, and healthcare.

Despite this tremendous success achieved by DL, data from different domains lead to various challenges to classical DL algorithms. Real-world time series data are usually irregular, high-dimensional, imperfect, non-Euclidean, or noisy, necessitating novel designs in DL architecture and training algorithms; therefore, it is of real value to delve into the principles of designing DL algorithms for various fields. The need for accuracy, transparency, and understandability in these models is not just academic; it has practical implications in real-world applications. This Special Issue encourages forecasting researchers to provide publicized datasets.

The objective of this Special Issue is to explore recent advances and techniques in the area of time series forecasting. Research topics of interest include (but are not limited to):

• Innovative deep learning models for time series forecasting;
• Techniques for improving the interpretability and transparency of deep learning models in time series analysis;
• Hybrid deep learning models for forecasting;
• Applications of advanced deep learning models for forecasting;
• Deep learning models for imperfect time series forecasting;
• Deep learning models for irregular time series forecasting;
• Missing value imputation for forecasting;
• Benchmark studies about deep learning models for forecasting.

Dr. Ruobin Gao
Dr. Maohan Liang
Dr. Xiaocai Zhang
Guest Editors

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• forecasting
• deep learning
• artificial intelligence
• machine learning
• neural networks