A review on outlier/anomaly detection in time series data
Recent advances in technology have brought major breakthroughs in data collection,
enabling a large amount of data to be gathered over time and thus generating time series …
enabling a large amount of data to be gathered over time and thus generating time series …
Spatio-temporal data mining: A survey of problems and methods
Large volumes of spatio-temporal data are increasingly collected and studied in diverse
domains, including climate science, social sciences, neuroscience, epidemiology …
domains, including climate science, social sciences, neuroscience, epidemiology …
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 …
Panoptic segmentation of satellite image time series with convolutional temporal attention networks
Unprecedented access to multi-temporal satellite imagery has opened new perspectives for
a variety of Earth observation tasks. Among them, pixel-precise panoptic segmentation of …
a variety of Earth observation tasks. Among them, pixel-precise panoptic segmentation of …
Explainable AI for clinical and remote health applications: a survey on tabular and time series data
Abstract Nowadays Artificial Intelligence (AI) has become a fundamental component of
healthcare applications, both clinical and remote, but the best performing AI systems are …
healthcare applications, both clinical and remote, but the best performing AI systems are …
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 …
Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets
The all-pairs-similarity-search (or similarity join) problem has been extensively studied for
text and a handful of other datatypes. However, surprisingly little progress has been made …
text and a handful of other datatypes. However, surprisingly little progress has been made …
A survey on time-series pre-trained models
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 …
practical applications. Deep learning models that rely on massive labeled data have been …
Transfer learning for time series classification
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 …
a source dataset, and then transferring the learned features (the network's weights) to a …
[PDF][PDF] Time-series extreme event forecasting with neural networks at uber
Accurate time-series forecasting during high variance segments (eg, holidays), is critical for
anomaly detection, optimal resource allocation, budget planning and other related tasks. At …
anomaly detection, optimal resource allocation, budget planning and other related tasks. At …