A survey on graph neural networks for time series: Forecasting, classification, imputation, and anomaly detection
Time series are the primary data type used to record dynamic system measurements and
generated in great volume by both physical sensors and online processes (virtual sensors) …
generated in great volume by both physical sensors and online processes (virtual sensors) …
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 …
Deep learning for time series classification and extrinsic regression: A current survey
Time Series Classification and Extrinsic Regression are important and challenging machine
learning tasks. Deep learning has revolutionized natural language processing and computer …
learning tasks. Deep learning has revolutionized natural language processing and computer …
aeon: a Python toolkit for learning from time series
Abstract aeon is a unified Python 3 library for all machine learning tasks involving time
series. The package contains modules for time series forecasting, classification, extrinsic …
series. The package contains modules for time series forecasting, classification, extrinsic …
Detection of the pipeline elbow erosion by percussion and deep learning
Elbows are commonly used in pipelines to change the direction of flow, and the pipeline
elbows are prone to erosion caused by the transported medium. Detection of the pipeline …
elbows are prone to erosion caused by the transported medium. Detection of the pipeline …
Timemae: Self-supervised representations of time series with decoupled masked autoencoders
Enhancing the expressive capacity of deep learning-based time series models with self-
supervised pre-training has become ever-increasingly prevalent in time series classification …
supervised pre-training has become ever-increasingly prevalent in time series classification …
Hydra: Competing convolutional kernels for fast and accurate time series classification
We demonstrate a simple connection between dictionary methods for time series
classification, which involve extracting and counting symbolic patterns in time series, and …
classification, which involve extracting and counting symbolic patterns in time series, and …
Unsupervised feature based algorithms for time series extrinsic regression
Abstract Time Series Extrinsic Regression (TSER) involves using a set of training time series
to form a predictive model of a continuous response variable that is not directly related to the …
to form a predictive model of a continuous response variable that is not directly related to the …
QCore: Data-efficient, on-device continual calibration for quantized models
We are witnessing an increasing availability of streaming data that may contain valuable
information on the underlying processes. It is thus attractive to be able to deploy machine …
information on the underlying processes. It is thus attractive to be able to deploy machine …
Time series classification: A review of algorithms and implementations
J Faouzi - Machine Learning (Emerging Trends and Applications), 2022 - inria.hal.science
Time series classification is a subfield of machine learning with numerous real-life
applications. Due to the temporal structure of the input data, standard machine learning …
applications. Due to the temporal structure of the input data, standard machine learning …