Deep learning for time series classification: a review
Abstract Time Series Classification (TSC) is an important and challenging problem in data
mining. With the increase of time series data availability, hundreds of TSC algorithms have …
mining. With the increase of time series data availability, hundreds of TSC algorithms have …
[PDF][PDF] 卷积神经网络研究综述
周飞燕， 金林鹏， 董军 - 计算机学报, 2017 - cjc.ict.ac.cn
摘要作为一个十余年来快速发展的崭新领域, 深度学习受到了越来越多研究者的关注,
它在特征提取和模型拟合上都有着相较于浅层模型显然的优势. 深度学习善于从原始输入数据中 …
它在特征提取和模型拟合上都有着相较于浅层模型显然的优势. 深度学习善于从原始输入数据中 …
Deep learning for sensor-based activity recognition: A survey
Sensor-based activity recognition seeks the profound high-level knowledge about human
activities from multitudes of low-level sensor readings. Conventional pattern recognition …
activities from multitudes of low-level sensor readings. Conventional pattern recognition …
Electricity price forecasting on the day-ahead market using machine learning
The price of electricity on the European market is very volatile. This is due both to its mode of
production by different sources, each with its own constraints (volume of production …
production by different sources, each with its own constraints (volume of production …
Unsupervised pre-training of a deep LSTM-based stacked autoencoder for multivariate time series forecasting problems
Currently, most real-world time series datasets are multivariate and are rich in dynamical
information of the underlying system. Such datasets are attracting much attention; therefore …
information of the underlying system. Such datasets are attracting much attention; therefore …
Time series classification from scratch with deep neural networks: A strong baseline
We propose a simple but strong baseline for time series classification from scratch with deep
neural networks. Our proposed baseline models are pure end-to-end without any heavy …
neural networks. Our proposed baseline models are pure end-to-end without any heavy …
Conditional time series forecasting with convolutional neural networks
We present a method for conditional time series forecasting based on an adaptation of the
recent deep convolutional WaveNet architecture. The proposed network contains stacks of …
recent deep convolutional WaveNet architecture. The proposed network contains stacks of …
Gated transformer networks for multivariate time series classification
M Liu, S Ren, S Ma, J Jiao, Y Chen, Z Wang… - arXiv preprint arXiv …, 2021 - arxiv.org
Deep learning model (primarily convolutional networks and LSTM) for time series
classification has been studied broadly by the community with the wide applications in …
classification has been studied broadly by the community with the wide applications in …
Human activity recognition using inertial sensors in a smartphone: An overview
The ubiquity of smartphones and the growth of computing resources, such as connectivity,
processing, portability, and power of sensing, have greatly changed people's lives. Today …
processing, portability, and power of sensing, have greatly changed people's lives. Today …
Precision medicine and artificial intelligence: a pilot study on deep learning for hypoglycemic events detection based on ECG
Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial
diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can …
diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can …