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of 424
pro vyhledávání: '"A. Vijaya Krishna"'
Autor:
Yalavarthi, Vijaya Krishna, Scholz, Randolf, Madhusudhanan, Kiran, Born, Stefan, Schmidt-Thieme, Lars
Probabilistic forecasting models for joint distributions of targets in irregular time series are a heavily under-researched area in machine learning with, to the best of our knowledge, only three models researched so far: GPR, the Gaussian Process Re
Externí odkaz:
http://arxiv.org/abs/2406.07246
Autor:
Klötergens, Christian, Yalavarthi, Vijaya Krishna, Stubbemann, Maximilian, Schmidt-Thieme, Lars
Irregularly sampled time series with missing values are often observed in multiple real-world applications such as healthcare, climate and astronomy. They pose a significant challenge to standard deep learning models that operate only on fully observ
Externí odkaz:
http://arxiv.org/abs/2405.03582
The use of automotive radars is gaining popularity as a means to enhance a vehicle's sensing capabilities. However, these radars can suffer from interference caused by transmissions from other radars mounted on nearby vehicles. To address this issue,
Externí odkaz:
http://arxiv.org/abs/2404.16253
Are EEG Sequences Time Series? EEG Classification with Time Series Models and Joint Subject Training
Autor:
Burchert, Johannes, Werner, Thorben, Yalavarthi, Vijaya Krishna, de Portugal, Diego Coello, Stubbemann, Maximilian, Schmidt-Thieme, Lars
As with most other data domains, EEG data analysis relies on rich domain-specific preprocessing. Beyond such preprocessing, machine learners would hope to deal with such data as with any other time series data. For EEG classification many models have
Externí odkaz:
http://arxiv.org/abs/2404.06966
Probabilistic forecasting of irregularly sampled multivariate time series with missing values is an important problem in many fields, including health care, astronomy, and climate. State-of-the-art methods for the task estimate only marginal distribu
Externí odkaz:
http://arxiv.org/abs/2402.06293
In the early observation period of a time series, there might be only a few historic observations available to learn a model. However, in cases where an existing prior set of datasets is available, Meta learning methods can be applicable. In this pap
Externí odkaz:
http://arxiv.org/abs/2307.09796
Autor:
Yalavarthi, Vijaya Krishna, Madhusudhanan, Kiran, Sholz, Randolf, Ahmed, Nourhan, Burchert, Johannes, Jawed, Shayan, Born, Stefan, Schmidt-Thieme, Lars
Forecasting irregularly sampled time series with missing values is a crucial task for numerous real-world applications such as healthcare, astronomy, and climate sciences. State-of-the-art approaches to this problem rely on Ordinary Differential Equa
Externí odkaz:
http://arxiv.org/abs/2305.12932
Publikováno v:
IEEE International Conference on BigData, 2023
Irregularly sampled time series data with missing values is observed in many fields like healthcare, astronomy, and climate science. Interpolation of these types of time series is crucial for tasks such as root cause analysis and medical diagnosis, a
Externí odkaz:
http://arxiv.org/abs/2210.02091
Asynchronous Time Series is a multivariate time series where all the channels are observed asynchronously-independently, making the time series extremely sparse when aligning them. We often observe this effect in applications with complex observation
Externí odkaz:
http://arxiv.org/abs/2208.11374
Autor:
Vijaya Krishna Kanaparthi
Publikováno v:
FinTech, Vol 3, Iss 1, Pp 151-172 (2024)
This research paper explores the complicated connection between uncertainty and the Markowitz asset allocation framework, specifically investigating how mistakes in estimating parameters significantly impact the performance of strategies during out-o
Externí odkaz:
https://doaj.org/article/7d1952fcd5c44d1dbd349366362991f8
