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Found 5 papers, 0 papers with code
Hankel low-rank approximation and completion in time series analysis and forecasting: a brief review
In this paper we offer a review and bibliography of work on Hankel low-rank approximation and completion, with particular emphasis on how this methodology can be used for time series analysis and forecasting.
Segmentation analysis and the recovery of queuing parameters via the Wasserstein distance: a study of administrative data for patients with chronic obstructive pulmonary disease
This work uses a data-driven approach to analyse how the resource requirements of patients with chronic obstructive pulmonary disease (COPD) may change, quantifying how those changes impact the hospital system with which the patients interact.
A novel initialisation based on hospital-resident assignment for the k-modes algorithm
This paper presents a new way of selecting an initial solution for the k-modes algorithm that allows for a notion of mathematical fairness and a leverage of the data that the common initialisations from literature do not.
Evolutionary Dataset Optimisation: learning algorithm quality through evolution
We instead aim to gain a richer picture of the performance of an algorithm by generating artificial data through genetic evolution, the purpose of which is to create populations of datasets for which a particular algorithm performs well on a given metric.
Structured low-rank matrix completion for forecasting in time series analysis
In this paper we consider the low-rank matrix completion problem with specific application to forecasting in time series analysis.
