Deep learning has significantly advanced time series forecasting through its
powerful capacity to capture sequence relationships. However, training these
models with the Mean Square Error (MSE) loss often results in over-smooth
predictions, making it challenging to handle the complexity and learn
high-entropy features from time series data with high variability and
unpredictability. In this work, we introduce a novel approach by tokenizing
time series values to train forecasting models via cross-entropy loss, while
considering the continuous nature of time series data. Specifically, we propose
Hierarchical Classification Auxiliary Network, HCAN, a general model-agnostic
component that can be integrated with any forecasting model. HCAN is based on a
Hierarchy-Aware Attention module that integrates multi-granularity high-entropy
features at different hierarchy levels. At each level, we assign a class label
for timesteps to train an Uncertainty-Aware Classifier. This classifier
mitigates the over-confidence in softmax loss via evidence theory. We also
implement a Hierarchical Consistency Loss to maintain prediction consistency
across hierarchy levels. Extensive experiments integrating HCAN with
state-of-the-art forecasting models demonstrate substantial improvements over
baselines on several real-world datasets. Code is available
at:this https URL

我们提出了一种使用交叉熵损失对时间序列数值进行分词训练预测模型的新方法，通过整合多层次的高熵特征，使用层次感知注意模块，分配类标签并使用不确定性感知分类器来减轻预测中的过度自信，同时通过层次一致性损失保持预测结果的一致性。在多个真实世界数据集上，将 HCAN 与先进的预测模型相结合进行了大量实验证明了显著的改进。