Time series analysis stands as a focal point within the data mining community, serving as a
cornerstone for extracting valuable insights crucial to a myriad of real-world applications …

Time series, characterized by a sequence of data points arranged in a discrete-time order,
are ubiquitous in real-world applications. Different from other modalities, time series present …

We introduce MOMENT, a family of open-source foundation models for general-purpose
time series analysis. Pre-training large models on time series data is challenging due to (1) …

Large language models (LLMs) are being applied to time series forecasting. But are
language models actually useful for time series? In a series of ablation studies on three …

Deep learning has contributed remarkably to the advancement of time series analysis. Still,
deep models can encounter performance bottlenecks in real-world data-scarce scenarios …

Data-driven autonomous driving motion generation tasks are frequently impacted by the
limitations of dataset size and the domain gap between datasets, which precludes their …

Large Language Models (LLMs) have seen significant use in domains such as natural
language processing and computer vision. Going beyond text, image and graphics, LLMs …

Deep models have demonstrated remarkable performance in time series forecasting.
However, due to the partially-observed nature of real-world applications, solely focusing on …

Time-series anomaly detection is a fundamental task across scientific fields and industries.
However, the field has long faced the``elephant in the room:''critical issues including flawed …

Recently, remarkable progress has been made over large language models (LLMs),
demonstrating their unprecedented capability in varieties of natural language tasks …