Results 81 to 90 of about 517,907 (286)
IEEE AccessThis study delves into the domain of dynamical systems, specifically the forecasting of dynamical time series defined through an evolution function. Traditional approaches in this area predict the future behavior of dynamical systems by inferring the ...
Akifumi Okuno +2 more
doaj +1 more source
Molecular Oncology, EarlyView.Keratin 19 (KRT19) is overexpressed in high‐grade serous ovarian cancer with high levels of Kallikrein‐related peptidases (KLK) 4–7 and is associated with poor survival. In vivo analyses demonstrate that elevated KRT19 increases peritoneal tumour burden.
Sophia Bielesch +13 more
wiley +1 more source
, 2016 We discuss Bayesian forecasting of increasingly high-dimensional time series, a key area of application of stochastic dynamic models in the financial industry and allied areas of business.
West, Mike, Xie, Meng, Zhao, Zoey Yi
core +1 more source
Molecular Oncology, EarlyView.We have established a humanized orthotopic patient‐derived xenograft (Hu‐oPDX) mouse model of high‐grade serous ovarian cancer (HGSOC) that recapitulates human tumor–immune interactions. Using combined anti‐PD‐L1/anti‐CD73 immunotherapy, we demonstrate the model's improved biological relevance and enhanced translational value for preclinical ...
Luka Tandaric +10 more
wiley +1 more source
On the Benefit of Using Time Series Features for Choosing a Forecasting Method [PDF]
In research of time series forecasting, a lot of uncertainty is still related to the question of which forecasting method to use in which situation. One thing is obvious: There is no single method that performs best on all time series.
Gabrys, Bogdan, Lemke, Christiane
core
Forecasting Stock Time-Series using Data Approximation and Pattern Sequence Similarity [PDF]
, 2013 Time series analysis is the process of building a model using statistical techniques to represent characteristics of time series data. Processing and forecasting huge time series data is a challenging task.
Iyengar, S. S. +7 more
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Machine-Learning Models for Sales Time Series Forecasting
Data, 2019 In this paper, we study the usage of machine-learning models for sales predictive analytics. The main goal of this paper is to consider main approaches and case studies of using machine learning for sales forecasting.
Bohdan M. Pavlyshenko
doaj +1 more source
TMC4 localizes to multiple taste cell types in the mouse taste papillae
FEBS Open Bio, EarlyView.Transmembrane channel‐like 4 (TMC4), a voltage‐dependent chloride channel, plays a critical role in amiloride‐insensitive salty taste transduction. TMC4 is broadly expressed in all mature taste cell types, suggesting a possible involvement of multiple cell types in this pathway.
Momo Murata +6 more
wiley +1 more source
On the predictive content of nonlinear transformations of lagged autoregression residuals and time series observations [PDF]
This study focuses on the question whether nonlinear transformation of lagged time series values and residuals are able to systematically improve the average forecasting performance of simple Autoregressive models.
Rossen, Anja
core
Time-series forecasting through recurrent topology
Communications EngineeringTime-series forecasting is a practical goal in many areas of science and engineering. Common approaches for forecasting future events often rely on highly parameterized or black-box models.
Taylor Chomiak, Bin Hu
doaj +1 more source