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

Stock market prediction has been a classical yet challenging problem, with the attention from
both economists and computer scientists. With the purpose of building an effective prediction …

The dramatic progresses of wireless technologies and wearable devices have significantly
promoted the development and popularity of smart home, while digital twin (DT) emerges as …

Deep learning performs remarkably well on many time series analysis tasks recently. The
superior performance of deep neural networks relies heavily on a large number of training …

This paper brings deep learning at the forefront of research into time series classification
(TSC). TSC is the area of machine learning tasked with the categorization (or labelling) of …

Most methods for time series classification that attain state-of-the-art accuracy have high
computational complexity, requiring significant training time even for smaller datasets, and …

This paper presents a critical approach to the non-intrusive load monitoring (NILM) problem,
by thoroughly reviewing the experimental framework of both legacy and state-of-the-art …

Time series analysis is a field of data science which is interested in analyzing sequences of
numerical values ordered in time. Time series are particularly interesting because they allow …

Electrocardiography (ECG) is a very common, non-invasive diagnostic procedure and its
interpretation is increasingly supported by algorithms. The progress in the field of automatic …

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 …