Current methods for time series forecasting struggle in the online scenario, since it is difficult to preserve long-term dependency while adapting short-term changes when data are arriving sequentially. Although some recent methods solve this problem by controlling the updates of latent states, they cannot disentangle the long/short-term states, leading to the inability to effectively adapt to nonstationary. To tackle this challenge, we propose a general framework to disentangle long/short-term states for online time series forecasting. Our idea is inspired by the observations where short-term changes can be led by unknown interventions like abrupt policies in the stock market. Based on this insight, we formalize a data generation process with unknown interventions on short-term states. Under mild assumptions, we further leverage the independence of short-term states led by unknown interventions to establish the identification theory to achieve the disentanglement of long/short-term states. Built on this theory, we develop a long short-term disentanglement model (LSTD) to extract the long/short-term states with long/short-term encoders, respectively. Furthermore, the LSTD model incorporates a smooth constraint to preserve the long-term dependencies and an interrupted dependency constraint to enforce the forgetting of short-term dependencies, together boosting the disentanglement of long/short-term states. Experimental results on several benchmark datasets show that our \textbf{LSTD} model outperforms existing methods for online time series forecasting, validating its efficacy in real-world applications.

Machine learning and deep learning have become essential skills for researchers in many fields, from computer science to biology to finance. With the explosion of data and the increasing demand for data-driven insights, the ability to understand and apply machine learning and deep learning techniques has become a critical advantage for researchers. However, learning these skills can be challenging, especially for those who are new to the field. In this article, I will share some of the top resources that can help researchers learn machine learning and deep learning effectively. One of the best ways to learn machine learning and deep learning is through online courses. There are many excellent courses available, including those from top universities like Stanford, MIT, and Carnegie Mellon.

ICML is the leading international machine learning conference and is supported by the International Machine Learning Society (IMLS). The talks for this event are currently behind a password firewall temporarily for QA.