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) …
The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances
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 …
target variable from ordered, real valued, attributes. Over recent years, a new set of TSC …
[HTML][HTML] Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry
Recent developments in maintenance modelling fueled by data-based approaches such as
machine learning (ML), have enabled a broad range of applications. In the automotive …
machine learning (ML), have enabled a broad range of applications. In the automotive …
A transformer-based framework for multivariate time series representation learning
We present a novel framework for multivariate time series representation learning based on
the transformer encoder architecture. The framework includes an unsupervised pre-training …
the transformer encoder architecture. The framework includes an unsupervised pre-training …
Methods and tools for causal discovery and causal inference
Causality is a complex concept, which roots its developments across several fields, such as
statistics, economics, epidemiology, computer science, and philosophy. In recent years, the …
statistics, economics, epidemiology, computer science, and philosophy. In recent years, the …
Minirocket: A very fast (almost) deterministic transform for time series classification
Rocket achieves state-of-the-art accuracy for time series classification with a fraction of the
computational expense of most existing methods by transforming input time series using …
computational expense of most existing methods by transforming input time series using …
Tslearn, a machine learning toolkit for time series data
tslearn is a general-purpose Python machine learning library for time series that offers tools
for pre-processing and feature extraction as well as dedicated models for clustering …
for pre-processing and feature extraction as well as dedicated models for clustering …
Inceptiontime: Finding alexnet for time series classification
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 …
(TSC). TSC is the area of machine learning tasked with the categorization (or labelling) of …
Electricity price forecasting on the day-ahead market using machine learning
The price of electricity on the European market is very volatile. This is due both to its mode of
production by different sources, each with its own constraints (volume of production …
production by different sources, each with its own constraints (volume of production …
ROCKET: exceptionally fast and accurate time series classification using random convolutional kernels
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 …
computational complexity, requiring significant training time even for smaller datasets, and …