
Monash Time Series Forecasting Archive
Many businesses and industries nowadays rely on large quantities of time...
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Elastic Similarity Measures for Multivariate Time Series Classification
Elastic similarity measures are a class of similarity measures specifica...
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Tight lower bounds for Dynamic Time Warping
Dynamic Time Warping (DTW) is a popular similarity measure for aligning ...
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Early Abandoning and Pruning for Elastic Distances
Elastic distances are key tools for time series analysis. Straightforwar...
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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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Better Short than Greedy: Interpretable Models through Optimal Rule Boosting
Rule ensembles are designed to provide a useful tradeoff between predic...
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Ensembles of Localised Models for Time Series Forecasting
With large quantities of data typically available nowadays, forecasting ...
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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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Discriminative, Generative and SelfSupervised Approaches for TargetAgnostic Learning
Supervised learning, characterized by both discriminative and generative...
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An Eager Splitting Strategy for Online Decision Trees
We study the effectiveness of replacing the split strategy for the state...
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Emergent and Unspecified Behaviors in Streaming Decision Trees
Hoeffding trees are the stateoftheart methods in decision tree learni...
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A Strong Baseline for Weekly Time Series Forecasting
Many businesses and industries require accurate forecasts for weekly tim...
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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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Time Series Regression
This paper introduces Time Series Regression (TSR): a littlestudied tas...
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Monash University, UEA, UCR Time Series Regression Archive
Time series research has gathered lots of interests in the last decade, ...
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A Bayesianinspired, deep learning, semisupervised domain adaptation technique for land cover mapping
Land cover maps are a vital input variable to many types of environmenta...
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ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels
Most methods for time series classification that attain stateoftheart...
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Time series classification for varying length series
Research into time series classification has tended to focus on the case...
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InceptionTime: Finding AlexNet for Time Series Classification
Time series classification (TSC) is the area of machine learning interes...
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DreamTime: Finding AlexNet for Time Series Classification
Time series classification (TSC) is the area of machine learning interes...
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TSCHIEF: 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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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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Proximity Forest: An effective and scalable distancebased classifier for time series
Research into the classification of time series has made enormous progre...
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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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An Incremental Construction of Deep Neuro Fuzzy System for Continual Learning of Nonstationary Data Streams
Existing fuzzy neural networks (FNNs) are mostly developed under a shall...
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InstanceDependent PU Learning by Bayesian Optimal Relabeling
When learning from positive and unlabelled data, it is a strong assumpti...
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On the Interrelationships among Drift rate, Forgetting rate, Bias/variance profile and Error
We propose two general and falsifiable hypotheses about expectations on ...
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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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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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Characterizing Concept Drift
Most machine learning models are static, but the world is dynamic, and i...
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Skopus: Exact discovery of the most interesting sequential patterns under Leverage
This paper presents a framework for exact discovery of the most interest...
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Geoffrey I. Webb
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