The UEA multivariate time series classification archive, 2018

Abstract

In 2002, the UCR time series classification archive was first released with sixteen datasets. It gradually expanded, until 2015 when it increased in size from 45 datasets to 85 datasets. In October 2018 more datasets were added, bringing the total to 128. The new archive contains a wide range of problems, including variable length series, but it still only contains univariate time series classification problems. One of the motivations for introducing the archive was to encourage researchers to perform a more rigorous evaluation of newly proposed time series classification (TSC) algorithms. It has worked: most recent research into TSC uses all 85 datasets to evaluate algorithmic advances. Research into multivariate time series classification, where more than one series are associated with each class label, is in a position where univariate TSC research was a decade ago. Algorithms are evaluated using very few datasets and claims of improvement are not based on statistical comparisons. We aim to address this problem by forming the first iteration of the MTSC archive, to be hosted at the website www.timeseriesclassification.com. Like the univariate archive, this formulation was a collaborative effort between researchers at the University of East Anglia (UEA) and the University of California, Riverside (UCR). The 2018 vintage consists of 30 datasets with a wide range of cases, dimensions and series lengths. For this first iteration of the archive we format all data to be of equal length, include no series with missing data and provide train/test splits.

Cited by in corpus (33)

• Self-attention for raw optical Satellite Time Series Classification
• HIVE-COTE 2.0: a new meta ensemble for time series classification
• Unsupervised Scalable Representation Learning for Multivariate Time Series
• HiPPO: Recurrent Memory with Optimal Polynomial Projections
• The Canonical Interval Forest (CIF) Classifier for Time Series Classification
• Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Survey
• Is rotation forest the best classifier for problems with continuous features?
• FlexConv: Continuous Kernel Convolutions with Differentiable Kernel Sizes
• Neural Transformation Learning for Deep Anomaly Detection Beyond Images
• Exathlon: A Benchmark for Explainable Anomaly Detection over Time Series
• CKConv: Continuous Kernel Convolution For Sequential Data
• UnICORNN: A recurrent model for learning very long time dependencies
• Monash University, UEA, UCR Time Series Extrinsic Regression Archive
• A Generalised Signature Method for Multivariate Time Series Feature Extraction
• Neural Controlled Differential Equations for Online Prediction Tasks
• Generalised Interpretable Shapelets for Irregular Time Series
• Adversarial Attacks on Multivariate Time Series
• Multi-Stage Prototype Learning for Interpretable Time Series Classification
• TimeAutoML: Autonomous Representation Learning for Multivariate Irregularly Sampled Time Series
• Hierarchical Clustering using Auto-encoded Compact Representation for Time-series Analysis
• Deep Reservoir Networks with Learned Hidden Reservoir Weights using Direct Feedback Alignment
• Discovering Subdimensional Motifs of Different Lengths in Large-Scale Multivariate Time Series
• TC-DTW: Accelerating Multivariate Dynamic Time Warping Through Triangle Inequality and Point Clustering
• SMATE: Semi-Supervised Spatio-Temporal Representation Learning on Multivariate Time Series
• Second-Order Neural ODE Optimizer
• Supervised Feature Subset Selection and Feature Ranking for Multivariate Time Series without Feature Extraction
• Multivariate Time Series Classification Using Spiking Neural Networks
• Long Expressive Memory for Sequence Modeling
• A self-organising eigenspace map for time series clustering
• Learnable Dynamic Temporal Pooling for Time Series Classification
• Time Series Classification Using Convolutional Neural Network On Imbalanced Datasets
• Mimic: An adaptive algorithm for multivariate time series classification
• Detecting Driver's Distraction using Long-term Recurrent Convolutional Network