Zobrazeno 1 - 10
of 31
pro vyhledávání: '"Yanchenko, Anna"'
Publikováno v:
In 1st ACM SIGSPATIAL International Workshop on Geospatial Anomaly Detection (GeoAnomalies'24), October 29, 2024, Atlanta, GA, USA
Spatiotemporal data consisting of timestamps, GPS coordinates, and IDs occurs in many settings. Modeling approaches for this type of data must address challenges in terms of sensor noise, uneven sampling rates, and non-persistent IDs. In this work, w
Externí odkaz:
http://arxiv.org/abs/2410.07161
Reply to Discussions of 'Multivariate Dynamic Modeling for Bayesian Forecasting of Business Revenue'
Autor:
Yanchenko, Anna K., Tierney, Graham, Lawson, Joseph, Hellmayr, Christoph, Cron, Andrew, West, Mike
We are most grateful to all discussants for their positive comments and many thought-provoking questions. In addition, the discussants provide a number of useful leads into various areas of the literatures on time series, forecasting and commercial a
Externí odkaz:
http://arxiv.org/abs/2304.00552
Autor:
Yanchenko, Anna K., Tierney, Graham, Lawson, Joseph, Hellmayr, Christoph, Cron, Andrew, West, Mike
Publikováno v:
Applied Stochastic Models in Business and Industry, forthcoming, 2022
Forecasting enterprise-wide revenue is critical to many companies and presents several challenges and opportunities for significant business impact. This case study is based on model developments to address these challenges for forecasting in a large
Externí odkaz:
http://arxiv.org/abs/2112.05678
Autor:
Yanchenko, Anna K., Soltani, Mohammadreza, Ravier, Robert J., Mukherjee, Sayan, Tarokh, Vahid
Understanding the features learned by deep models is important from a model trust perspective, especially as deep systems are deployed in the real world. Most recent approaches for deep feature understanding or model explanation focus on highlighting
Externí odkaz:
http://arxiv.org/abs/2106.00110
We present a case study and methodological developments in large-scale hierarchical dynamic modeling for personalized prediction in commerce. The context is supermarket sales, where improved forecasting of customer/household-specific purchasing behav
Externí odkaz:
http://arxiv.org/abs/2101.03408
Autor:
Yanchenko, Anna K.
Orchestral concert programming is a challenging, yet critical task for expanding audience engagement and is usually driven by qualitative heuristics and common musical practices. Quantitative analysis of orchestral programming has been limited, but h
Externí odkaz:
http://arxiv.org/abs/2009.07887
Autor:
Yanchenko, Anna K., Mukherjee, Sayan
Time series with long-term structure arise in a variety of contexts and capturing this temporal structure is a critical challenge in time series analysis for both inference and forecasting settings. Traditionally, state space models have been success
Externí odkaz:
http://arxiv.org/abs/2006.06553
Autor:
Yanchenko, Anna K., Hoff, Peter D.
Publikováno v:
Annals of Applied Statistics, Volume 14, Number 4 (2020), 1581-1603
Quantification of stylistic differences between musical artists is of academic interest to the music community, and is also useful for other applications such as music information retrieval and recommendation systems. Information about stylistic diff
Externí odkaz:
http://arxiv.org/abs/2004.13870
Autor:
Yanchenko, Anna K., Mukherjee, Sayan
Algorithmic composition of music has a long history and with the development of powerful deep learning methods, there has recently been increased interest in exploring algorithms and models to create art. We explore the utility of state space models,
Externí odkaz:
http://arxiv.org/abs/1708.03822
Publikováno v:
In Proceedings of the 3rd ACM on International Workshop on Security And Privacy Analytics (IWSPA 2017). ACM, New York, NY, USA, 45-53
Each year, thousands of software vulnerabilities are discovered and reported to the public. Unpatched known vulnerabilities are a significant security risk. It is imperative that software vendors quickly provide patches once vulnerabilities are known
Externí odkaz:
http://arxiv.org/abs/1707.08015