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 …

Time Series Classification and Extrinsic Regression are important and challenging machine
learning tasks. Deep learning has revolutionized natural language processing and computer …

Transfer learning for deep neural networks is the process of first training a base network on
a source dataset, and then transferring the learned features (the network's weights) to a …

Deep learning model (primarily convolutional networks and LSTM) for time series
classification has been studied broadly by the community with the wide applications in …

Time series data usually contains local and global patterns. Most of the existing feature
networks focus on local features rather than the relationships among them. The latter is also …

Time-Series Mining (TSM) is an important research area since it shows great potential in
practical applications. Deep learning models that rely on massive labeled data have been …

Learning good representations without supervision is still an open issue in machine
learning, and is particularly challenging for speech signals, which are often characterized by …

Time Series Classification (TSC) problems are encountered in many real life data mining
tasks ranging from medicine and security to human activity recognition and food safety. With …

With the rapid increase of data availability, time series classification (TSC) has arisen in a
wide range of fields and drawn great attention of researchers. Recently, hundreds of TSC …

For time series classification task using 1D-CNN, the selection of kernel size is critically
important to ensure the model can capture the right scale salient signal from a long time …