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08/18/2023
CARLA: A Self-supervised Contrastive Representation Learning Approach for Time Series Anomaly Detection
We introduce a Self-supervised Contrastive Representation Learning Appro...
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08/02/2023
QUANT: A Minimalist Interval Method for Time Series Classification
We show that it is possible to achieve the same accuracy, on average, as...
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07/07/2023
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 meas...
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05/26/2023
Improving Position Encoding of Transformers for Multivariate Time Series Classification
Transformers have demonstrated outstanding performance in many applicati...
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05/19/2023
An Approach to Multiple Comparison Benchmark Evaluations that is Stable Under Manipulation of the Comparate Set
The measurement of progress using benchmarks evaluations is ubiquitous i...
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04/12/2023
Proximity Forest 2.0: A new effective and scalable similarity-based classifier for time series
Time series classification (TSC) is a challenging task due to the divers...
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02/06/2023
Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey
Time Series Classification and Extrinsic Regression are important and ch...
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01/24/2023
Parameterizing the cost function of Dynamic Time Warping with application to time series classification
Dynamic Time Warping (DTW) is a popular time series distance measure tha...
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11/16/2022
SETAR-Tree: A Novel and Accurate Tree Algorithm for Global Time Series Forecasting
Threshold Autoregressive (TAR) models have been widely used by statistic...
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11/09/2022
Deep Learning for Time Series Anomaly Detection: A Survey
Time series anomaly detection has applications in a wide range of resear...
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03/25/2022
HYDRA: Competing convolutional kernels for fast and accurate time series classification
We demonstrate a simple connection between dictionary methods for time s...
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12/08/2021
Estimating Divergences in High Dimensions
The problem of estimating the divergence between 2 high dimensional dist...
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11/26/2021
Amercing: An Intuitive, Elegant and Effective Constraint for Dynamic Time Warping
Dynamic Time Warping (DTW), and its constrained (CDTW) and weighted (WDT...
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05/14/2021
Monash Time Series Forecasting Archive
Many businesses and industries nowadays rely on large quantities of time...
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02/20/2021
Elastic Similarity Measures for Multivariate Time Series Classification
Elastic similarity measures are a class of similarity measures specifica...
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02/14/2021
Tight lower bounds for Dynamic Time Warping
Dynamic Time Warping (DTW) is a popular similarity measure for aligning ...
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02/10/2021
Early Abandoning and Pruning for Elastic Distances
Elastic distances are key tools for time series analysis. Straightforwar...
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01/31/2021
MultiRocket: Effective summary statistics for convolutional outputs in time series classification
Rocket and MiniRocket, while two of the fastest methods for time series ...
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01/21/2021
Better Short than Greedy: Interpretable Models through Optimal Rule Boosting
Rule ensembles are designed to provide a useful trade-off between predic...
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12/30/2020
Ensembles of Localised Models for Time Series Forecasting
With large quantities of data typically available nowadays, forecasting ...
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12/16/2020
MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series Classification
Until recently, the most accurate methods for time series classification...
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11/12/2020
Discriminative, Generative and Self-Supervised Approaches for Target-Agnostic Learning
Supervised learning, characterized by both discriminative and generative...
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10/20/2020
An Eager Splitting Strategy for Online Decision Trees
We study the effectiveness of replacing the split strategy for the state...
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10/16/2020
Emergent and Unspecified Behaviors in Streaming Decision Trees
Hoeffding trees are the state-of-the-art methods in decision tree learni...
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10/16/2020
A Strong Baseline for Weekly Time Series Forecasting
Many businesses and industries require accurate forecasts for weekly tim...
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10/11/2020
Early Abandoning PrunedDTW and its application to similarity search
The Dynamic Time Warping ("DTW") distance is widely used in time series ...
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06/23/2020
Time Series Regression
This paper introduces Time Series Regression (TSR): a little-studied tas...
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06/19/2020
Monash University, UEA, UCR Time Series Regression Archive
Time series research has gathered lots of interests in the last decade, ...
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05/25/2020
A Bayesian-inspired, deep learning, semi-supervised domain adaptation technique for land cover mapping
Land cover maps are a vital input variable to many types of environmenta...
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10/29/2019
ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels
Most methods for time series classification that attain state-of-the-art...
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10/10/2019
Time series classification for varying length series
Research into time series classification has tended to focus on the case...
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09/11/2019
InceptionTime: Finding AlexNet for Time Series Classification
Time series classification (TSC) is the area of machine learning interes...
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09/11/2019
DreamTime: Finding AlexNet for Time Series Classification
Time series classification (TSC) is the area of machine learning interes...
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06/25/2019
TS-CHIEF: A Scalable and Accurate Forest Algorithm for Time Series Classification
Time Series Classification (TSC) has seen enormous progress over the las...
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11/26/2018
Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series
New remote sensing sensors acquire now high spatial and spectral Satelli...
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08/31/2018
Proximity Forest: An effective and scalable distance-based classifier for time series
Research into the classification of time series has made enormous progre...
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08/29/2018
Elastic bands across the path: A new framework and methods to lower bound DTW
There has been renewed recent interest in developing effective lower bou...
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08/26/2018
An Incremental Construction of Deep Neuro Fuzzy System for Continual Learning of Non-stationary Data Streams
Existing fuzzy neural networks (FNNs) are mostly developed under a shall...
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08/07/2018
Instance-Dependent PU Learning by Bayesian Optimal Relabeling
When learning from positive and unlabelled data, it is a strong assumpti...
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01/29/2018
On the Inter-relationships among Drift rate, Forgetting rate, Bias/variance profile and Error
We propose two general and falsifiable hypotheses about expectations on ...
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09/12/2017
Specious rules: an efficient and effective unifying method for removing misleading and uninformative patterns in association rule mining
We present theoretical analysis and a suite of tests and procedures for ...
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08/25/2017
Accurate parameter estimation for Bayesian Network Classifiers using Hierarchical Dirichlet Processes
This paper introduces a novel parameter estimation method for the probab...
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11/12/2015
Characterizing Concept Drift
Most machine learning models are static, but the world is dynamic, and i...
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06/26/2015