Time Series Forecasting plays a crucial role in various fields such as
industrial equipment maintenance, meteorology, energy consumption, traffic flow
and financial investment. However, despite their considerable advantages over
traditional statistical approaches, current deep learning-based predictive
models often exhibit a significant deviation between their forecasting outcomes
and the ground truth. This discrepancy is largely due to an insufficient
emphasis on extracting the sequence's latent information, particularly its
global information within the frequency domain and the relationship between
different variables. To address this issue, we propose a novel model
Frequency-domain Attention In Two Horizons, which decomposes time series into
trend and seasonal components using a multi-scale sequence adaptive
decomposition and fusion architecture, and processes them separately. FAITH
utilizes Frequency Channel feature Extraction Module and Frequency Temporal
feature Extraction Module to capture inter-channel relationships and temporal
global information in the sequence, significantly improving its ability to
handle long-term dependencies and complex patterns. Furthermore, FAITH achieves
theoretically linear complexity by modifying the time-frequency domain
transformation method, effectively reducing computational costs. Extensive
experiments on 6 benchmarks for long-term forecasting and 3 benchmarks for
short-term forecasting demonstrate that FAITH outperforms existing models in
many fields, such as electricity, weather and traffic, proving its
effectiveness and superiority both in long-term and short-term time series
forecasting tasks. Our codes and data are available at
this https URL

提出了一种新的模型 FAITH，通过多尺度序列自适应分解和融合架构将时间序列分解为趋势和季节成分并分别处理，利用频域进行特征提取和处理，显著提高了处理长期依赖和复杂模式的能力，在长期和短期的时间序列预测任务中优于现有模型。

FAITH: 时间序列预测中的频域关注在两个视野中

FAITH: Frequency-domain Attention In Two Horizons for Time Series  Forecasting

Deep learning-based predictive models, leveraging Electronic Health Records
(EHR), are receiving increasing attention in healthcare. An effective
representation of a patient's EHR should hierarchically encompass both the
temporal relationships between historical visits and medical events, and the
inherent structural information within these elements. Existing patient
representation methods can be roughly categorized into sequential
representation and graphical representation. The sequential representation
methods focus only on the temporal relationships among longitudinal visits. On
the other hand, the graphical representation approaches, while adept at
extracting the graph-structured relationships between various medical events,
fall short in effectively integrate temporal information. To capture both types
of information, we model a patient's EHR as a novel temporal heterogeneous
graph. This graph includes historical visits nodes and medical events nodes. It
propagates structured information from medical event nodes to visit nodes and
utilizes time-aware visit nodes to capture changes in the patient's health
status. Furthermore, we introduce a novel temporal graph transformer (TRANS)
that integrates temporal edge features, global positional encoding, and local
structural encoding into heterogeneous graph convolution, capturing both
temporal and structural information. We validate the effectiveness of TRANS
through extensive experiments on three real-world datasets. The results show
that our proposed approach achieves state-of-the-art performance.

利用深度学习的预测模型，基于电子病历（EHR），在医疗保健领域备受关注。我们提出了一种新颖的时间异构图模型，将病人的 EHR 建模成历史访问节点和医疗事件节点，并利用时间感知访问节点来捕捉患者健康状况的变化，同时通过将时间边特征、全局位置编码和局部结构编码整合到异构图卷积中，捕捉了时间和结构信息。通过对三个真实数据集进行广泛实验，我们证实了 TRANS 的有效性，结果表明我们提出的方法达到了最先进的性能。