Large models for time series and spatio-temporal data: A survey and outlook
Temporal data, notably time series and spatio-temporal data, are prevalent in real-world
applications. They capture dynamic system measurements and are produced in vast …
applications. They capture dynamic system measurements and are produced in vast …
Symphony in the latent space: Provably integrating high-dimensional techniques with non-linear machine learning models
Q Wu, J Li, Z Liu, Y Li, M Cucuringu - … of the AAAI Conference on Artificial …, 2023 - ojs.aaai.org
This paper revisits building machine learning algorithms that involve interactions between
entities, such as those between financial assets in an actively managed portfolio, or …
entities, such as those between financial assets in an actively managed portfolio, or …
[HTML][HTML] An entropy-based approach for a robust least squares spline approximation
L Brugnano, D Giordano, F Iavernaro… - Journal of Computational …, 2024 - Elsevier
We consider the weighted least squares spline approximation of a noisy dataset. By
interpreting the weights as a probability distribution, we maximize the associated entropy …
interpreting the weights as a probability distribution, we maximize the associated entropy …
TimeAutoDiff: Combining Autoencoder and Diffusion model for time series tabular data synthesizing
In this paper, we leverage the power of latent diffusion models to generate synthetic time
series tabular data. Along with the temporal and feature correlations, the heterogeneous …
series tabular data. Along with the temporal and feature correlations, the heterogeneous …