Kairos is a flexible and parameter-efficient Time Series Foundation Model (TSFM) designed to handle the dynamic and heterogeneous nature of real-world time series data. Unlike existing models that ...

Have you ever imagined predicting your company’s revenue for the coming months based on past data? This is the power of time series forecasting, which analyzes data organized chronologically, such as ...

College of Mathematical and Statistics, Sichuan University of Science and Engineering, Zigong, China. With the rapid development of the global economy and the acceleration of urbanization, ...

HALO works by utilizing a pair of modules to represent both the visit- and code-level structures of a patient record. First, it uses a coarse, visit-level module to factorize the probability along ...

Many research questions in Earth and environmental sciences are inherently causal, requiring robust analyses to establish whether and how changes in one variable cause changes in another. Causal ...

Time series forecasting is a fundamental task in data science, applied statistics, and econometrics. With time series forecasting we aim to predict the future values of time series datasets. A time ...

In 2018–2020, meteorological droughts over Northwestern Europe caused severe declines in groundwater heads with significant damage to groundwater-dependent ecosystems and agriculture. The response of ...

VAR models are different from univariate autoregressive models because they allow analysis and make predictions on multivariate time series data. Vector autoregression (VAR) is a statistical model for ...

The era of modern financial data modeling seeks machine learning techniques which are suitable for noisy and non-stationary big data. We demonstrate how a general class of exponential smoothed ...