Results 71 to 80 of about 3,433,600 (309)
Modelling Stabilometric Time Series [PDF]
, 2010 Stabilometry is a branch of medicine that studies balance-related human functions. Stabilometric systems generate time series. The analysis of these time series using data mining techniques can be very useful for domain experts.
Caraça-Valente Hernández, Juan Pedro +3 more
core +1 more source
Neural Network Ensembles for Time Series Prediction [PDF]
, 2007 Rapidly evolving businesses generate massive amounts of time-stamped data sequences and defy a demand for massively multivariate time series analysis. For such data the predictive engine shifts from the historical auto-regression to modelling complex
Ruta, Dymitr +3 more
core +1 more source
time-series-nmf: Non-negative Matrix Factorization for Time Series [PDF]
, 2020 time-series-nmf is a Python package implementing non-negative matrix factorization for time series data. Currently, it supports a version with Tikhonov regularization and sparse constraints as proposed by Fabregat R.. et. al. and implemented in Matlab in
Valentina Staneva, Wu-Jung Lee
core +1 more source
An Efficient Green AI Approach to Time Series Forecasting Based on Deep Learning
Big Data and Cognitive ComputingTime series forecasting is undoubtedly a key area in machine learning due to the numerous fields where it is crucial to estimate future data points of sequences based on a set of previously observed values.
Luis Balderas +2 more
doaj +1 more source
Solid Pseudopapillary Neoplasm of the Pancreas in Children and Adolescents: Expert Recommendations
Pediatric Blood &Cancer, EarlyView.ABSTRACT Solid pseudopapillary neoplasm of the pancreas (SPN) is a rare low‐grade malignant exocrine pancreatic tumor, mostly discovered during the second decade of life in females, with a very good prognosis, provided microscopically complete surgical excision is achieved.
Sabine Irtan +18 more
wiley +1 more source
Analyzing Multimodal Time Series as Dynamical Systems [PDF]
, 2010 We propose a novel approach to discovering latent structures from multimodal time series. We view a time series as observed data from an underlying dynamical system. In this way, analyzing multimodal time series can be viewed as finding latent structures
Chen Yu +3 more
core +1 more source
Impact of Metastatic Patterns on Survival and Response to Therapy in Neuroblastoma
Pediatric Blood &Cancer, EarlyView.ABSTRACT Background While the presence of metastases in neuroblastoma (NB) is a well‐established prognostic factor, the clinical significance of dissemination patterns and tumour burden and their impact on response and survival remains poorly understood.
Mariona Morell‐Daniel +15 more
wiley +1 more source
Pediatric Blood &Cancer, EarlyView.ABSTRACT Neurodevelopmental and neurocognitive difficulties are prevalent among individuals with sickle cell disease and warrant prompt identification and support. This Special Report provides an executive summary of standards and recommendations for surveillance, screening, and evaluation for development and cognition across the lifespan developed by ...
Alyssa M. Schlenz +12 more
wiley +1 more source
lucaspauker/hmi-time-series-analysis: Time Series Analysis with HMI SHARP data [PDF]
, 2019 <p>Code for my physics summer research project studying the relationship between time series evolution of magnetic variables on the sun and solar flaring.</p ...
Monica Bobra +5 more
core +1 more source
CO and CO2 daily time series and time series of wind speed
Data in Brief, 2019 The global CO2 has increased rapidly because of the vast use of fossil fuels over past 40 years. CO, co-emitted with CO2, also increased during this period.
Changgong Shan, Wei Wang
doaj +1 more source