Abstract Time Series Classification (TSC) involves building predictive models for a discrete
target variable from ordered, real valued, attributes. Over recent years, a new set of TSC …

In 2017, a research paper (Bagnall et al. Data Mining and Knowledge Discovery 31 (3): 606-
660.) compared 18 Time Series Classification (TSC) algorithms on 85 datasets from the …

Most of state of the art methods applied on time series consist of deep learning methods that
are too complex to be interpreted. This lack of interpretability is a major drawback, as several …

Fully convolutional neural networks (FCNs) have been shown to achieve the state-of-the-art
performance on the task of classifying time series sequences. We propose the augmentation …

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 …

We present sktime--a new scikit-learn compatible Python library with a unified interface for
machine learning with time series. Time series data gives rise to various distinct but closely …

In the last 5 years there have been a large number of new time series classification
algorithms proposed in the literature. These algorithms have been evaluated on subsets of …

Discovering unexpected and useful patterns in databases is a fundamental data mining task.
In recent years, a trend in data mining has been to design algorithms for discovering …

Time series data is increasingly used in a wide range of fields, and it is often relied on in
crucial applications and high-stakes decision-making. For instance, sensors generate time …

Clustering is a solution for classifying enormous data when there is not any early knowledge
about classes. With emerging new concepts like cloud computing and big data and their vast …