标题： 深度卷积时间序列解释器 显示英文标题

标题： DCIts -- Deep Convolutional Interpreter for time series

摘要： 我们介绍了一种用于多变量时间序列预测的可解释深度学习模型，该模型优先考虑预测性能和可解释性——理解复杂物理现象的关键需求。 我们的模型不仅匹配而且常常超越现有的可解释性方法，在不牺牲准确性的情况下实现这一点。 通过广泛的实验，我们展示了它识别对未来值预测贡献最大的相关时间序列和滞后的能力，并为其预测提供了直观且透明的解释。 为了尽量减少人工监督的需求，该模型的设计使人们能够稳健地确定最佳窗口大小，以在尽可能小的时间范围内捕获所有必要的交互作用。 此外，它还有效识别最优模型阶数，在引入高阶项时平衡复杂性。 这些进步对建模和理解动态系统具有重要意义，使该模型成为应用物理学家和计算物理学家的宝贵工具。 显示英文摘要

摘要： We introduce an interpretable deep learning model for multivariate time series forecasting that prioritizes both predictive performance and interpretability - key requirements for understanding complex physical phenomena. Our model not only matches but often surpasses existing interpretability methods, achieving this without compromising accuracy. Through extensive experiments, we demonstrate its ability to identify the most relevant time series and lags that contribute to forecasting future values, providing intuitive and transparent explanations for its predictions. To minimize the need for manual supervision, the model is designed so one can robustly determine the optimal window size that captures all necessary interactions within the smallest possible time frame. Additionally, it effectively identifies the optimal model order, balancing complexity when incorporating higher-order terms. These advancements hold significant implications for modeling and understanding dynamic systems, making the model a valuable tool for applied and computational physicists.