The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances
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 …
algorithms proposed in the literature. These algorithms have been evaluated on subsets of …
Bake off redux: a review and experimental evaluation of recent time series classification algorithms
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 …
660.) compared 18 Time Series Classification (TSC) algorithms on 85 datasets from the …
Imaging time-series to improve classification and imputation
Inspired by recent successes of deep learning in computer vision, we propose a novel
framework for encoding time series as different types of images, namely, Gramian Angular …
framework for encoding time series as different types of images, namely, Gramian Angular …
Multi-scale convolutional neural networks for time series classification
Time series classification (TSC), the problem of predicting class labels of time series, has
been around for decades within the community of data mining and machine learning, and …
been around for decades within the community of data mining and machine learning, and …
Explainable artificial intelligence (xai) on timeseries data: A survey
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 …
are too complex to be interpreted. This lack of interpretability is a major drawback, as several …
Tapnet: Multivariate time series classification with attentional prototypical network
With the advance of sensor technologies, the Multivariate Time Series classification (MTSC)
problem, perhaps one of the most essential problems in the time series data mining domain …
problem, perhaps one of the most essential problems in the time series data mining domain …
[PDF][PDF] Encoding time series as images for visual inspection and classification using tiled convolutional neural networks
Inspired by recent successes of deep learning in computer vision and speech recognition,
we propose a novel framework to encode time series data as different types of images …
we propose a novel framework to encode time series data as different types of images …
Data augmentation for time series classification using convolutional neural networks
A Le Guennec, S Malinowski… - ECML/PKDD workshop on …, 2016 - shs.hal.science
Time series classification has been around for decades in the data-mining and machine
learning communities. In this paper, we investigate the use of convolutional neural networks …
learning communities. In this paper, we investigate the use of convolutional neural networks …
Classification of time-series images using deep convolutional neural networks
Convolutional Neural Networks (CNN) has achieved a great success in image recognition
task by automatically learning a hierarchical feature representation from raw data. While the …
task by automatically learning a hierarchical feature representation from raw data. While the …
The BOSS is concerned with time series classification in the presence of noise
P Schäfer - Data Mining and Knowledge Discovery, 2015 - Springer
Similarity search is one of the most important and probably best studied methods for data
mining. In the context of time series analysis it reaches its limits when it comes to mining raw …
mining. In the context of time series analysis it reaches its limits when it comes to mining raw …