Long-term time series forecasting is a vital task and has a wide range of real applications. Recent methods focus on capturing the underlying patterns from one single domain (e.g. the time domain or the frequency domain), and have not taken a holistic view to process long-term time series from the time-frequency domains. In this paper, we propose a Time-Frequency Enhanced Decomposed Network (TFDNet) to capture both the long-term underlying patterns and temporal periodicity from the time-frequency domain. In TFDNet, we devise a multi-scale time-frequency enhanced encoder backbone and develop two separate trend and seasonal time-frequency blocks to capture the distinct patterns within the decomposed trend and seasonal components in multi-resolutions. Diverse kernel learning strategies of the kernel operations in time-frequency blocks have been explored, by investigating and incorporating the potential different channel-wise correlation patterns of multivariate time series. Experimental evaluation of eight datasets from five benchmark domains demonstrated that TFDNet is superior to state-of-the-art approaches in both effectiveness and efficiency.

翻译：长期时间序列预测是一项至关重要的任务，具有广泛的实际应用。近期方法主要专注于从单一域（如时域或频域）捕捉潜在模式，尚未从时频域的整体视角出发处理长期时间序列。本文提出一种时频增强分解网络（TFDNet），用于从时频域同时捕捉长期潜在模式与时间周期性。在TFDNet中，我们设计了一个多尺度时频增强编码器主干，并开发了两个独立的趋势与时频模块，以分别捕捉分解后趋势分量和季节分量在多分辨率下的不同模式。通过探究并融合多变量时间序列中潜在的不同通道间相关性模式，我们探索了时频模块中核操作的不同核学习策略。在来自五个基准领域的八个数据集上的实验评估表明，TFDNet在有效性和效率上均优于现有最先进方法。