Efficiently modeling spatio-temporal (ST) physical processes and observations
presents a challenging problem for the deep learning community. Many recent
studies have concentrated on meticulously reconciling various advantages,
leading to designed models that are neither simple nor practical. To address
this issue, this paper presents a systematic study on existing shortcomings
faced by off-the-shelf models, including lack of local fidelity, poor
prediction performance over long time-steps,low scalability, and inefficiency.
To systematically address the aforementioned problems, we propose an
EarthFarseer, a concise framework that combines parallel local convolutions and
global Fourier-based transformer architectures, enabling dynamically capture
the local-global spatial interactions and dependencies. EarthFarseer also
incorporates a multi-scale fully convolutional and Fourier architectures to
efficiently and effectively capture the temporal evolution. Our proposal
demonstrates strong adaptability across various tasks and datasets, with fast
convergence and better local fidelity in long time-steps predictions. Extensive
experiments and visualizations over eight human society physical and natural
physical datasets demonstrates the state-of-the-art performance of
EarthFarseer. We release our code at
this https URL

通过结合本地卷积和全局基于傅立叶变换的 Transformer 架构，EarthFarseer 提出了一个简洁的框架，以动态捕捉本地 - 全局空间交互和依赖关系，并通过多尺度全卷积和傅立叶架构高效地捕捉时间演化。在八个人类社会物理和自然物理数据集上的广泛实验和可视化证明了 EarthFarseer 的最先进性能。

Earthfarseer：一种多功能的时空动力系统建模方法

Earthfarseer: Versatile Spatio-Temporal Dynamical Systems Modeling in  One Model

Machine learning and especially deep learning have garneredtremendous
popularity in recent years due to their increased performanceover other
methods. The availability of large amount of data has aidedin the progress of
deep learning. Nevertheless, deep learning models areopaque and often seen as
black boxes. Thus, there is an inherent need tomake the models interpretable,
especially so in the medical domain. Inthis work, we propose a locally
interpretable method, which is inspiredby one of the recent tools that has
gained a lot of interest, called localinterpretable model-agnostic explanations
(LIME). LIME generates singleinstance level explanation by artificially
generating a dataset aroundthe instance (by randomly sampling and using
perturbations) and thentraining a local linear interpretable model. One of the
major issues inLIME is the instability in the generated explanation, which is
caused dueto the randomly generated dataset. Another issue in these kind of
localinterpretable models is the local fidelity. We propose novel
modificationsto LIME by employing an autoencoder, which serves as a better
weightingfunction for the local model. We perform extensive comparisons
withdifferent datasets and show that our proposed method results in
bothimproved stability, as well as local fidelity.

本文提出了一种改进的可解释深度学习模型的方法，通过使用自动编码器来加权，从而提高了 LIME 的稳定性和本地保真度。