TIME series classification research and applications have recently received a lot of attention.With the rapid advancement of technological devices,time series data are being collected by a wide variety of devices,resu...TIME series classification research and applications have recently received a lot of attention.With the rapid advancement of technological devices,time series data are being collected by a wide variety of devices,resulting in a wide range of research and applications.Various fields of studies,ranging from healthcare to weather readings,require time series classification.Activity and action recognition using time series data from fitness trackers are commonly being practiced.Today,a new generation of algorithms and techniques are being used to classify time series data that are extensively available to the research community.For example,recently deep learning techniques that utilize convolutions and recurrent neural networks are being used to classify epileptic seizures from EEG recordings made available at the University of California-Irvine data repository.展开更多
文摘TIME series classification research and applications have recently received a lot of attention.With the rapid advancement of technological devices,time series data are being collected by a wide variety of devices,resulting in a wide range of research and applications.Various fields of studies,ranging from healthcare to weather readings,require time series classification.Activity and action recognition using time series data from fitness trackers are commonly being practiced.Today,a new generation of algorithms and techniques are being used to classify time series data that are extensively available to the research community.For example,recently deep learning techniques that utilize convolutions and recurrent neural networks are being used to classify epileptic seizures from EEG recordings made available at the University of California-Irvine data repository.
