Suppose we are considering a multivariate event sequence. What caused a specific event to happen? Which variables are causes of each other?

Reward allocation, also known as the credit assignment problem, has been an important topic in economics, engineering, and machine learning.

A major obstacle to the advancements of machine learning models in marine science, particularly in sonar imagery analysis, is the scarcity of AI-ready datasets. While there have been efforts to make AI-ready sonar image dataset publicly available, they suffer from limitations in terms of environment setting and scale.

In long-term time series forecasting (L TSF) tasks, an increasing number of works have acknowledged that discrete time series originate from continuous dynamic systems and have attempted to model their underlying dynamics. Recognizing the chaotic nature of real-world data, our model, Attraos, incorporates chaos theory into L TSF, perceiving real-world time series as low-dimensional observations from unknown high-dimensional chaotic dynamical systems. Under the concept of attractor invariance, Attraos utilizes non-parametric Phase Space Reconstruction embedding along with a novel multi-resolution dynamic memory unit to memorize historical dynamical structures, and evolves by a frequency-enhanced local evolution strategy. Detailed theoretical analysis and abundant empirical evidence consistently show that Attraos outperforms various L TSF methods on mainstream L TSF datasets and chaotic datasets with only one-twelfth of the parameters compared to PatchTST.

This relies on three key ingredients: 1. an estimator of