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 var…
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
169,042 papers · 148 categories
Trend · papers per month
48 results for “UCR/UEA archive”
Deep neural networks outperform traditional ensemble methods in time series classification.
problem Deep learning models struggle to match traditional ensemble methods in time series classification.
method Developed an ensemble of 60 deep learning models to improve time series classification performance.
result The proposed Neural Network Ensemble (NNE) outperforms current state-of-the-art methods.
Introduces a new benchmark for time series extrinsic regression.
problem Predicting a single continuous value from univariate or multivariate time series, not necessarily related to the predictor.
method Developed a new benchmarking archive for time series extrinsic regression.
result Initial benchmarking of existing models on the new TSER datasets.
Revisit Time Series Classification Benchmark: The Impact of Temporal Information for Classificationcs.LG
Temporal information impacts only a fraction of time series datasets, skewing benchmark evaluations.
problem Temporal information's impact on time series classification is often overestimated.
method Permutation tests on UCR archive to identify datasets where temporal info is irrelevant.
result Many tabular datasets perform well without temporal info, skewing benchmark evaluations.
Computer vision SSL methods show effectiveness on time series data.
problem Evaluate if computer vision SSL frameworks are effective on time series data.
method Evaluated on UCR and UEA archives, proposed a new method improving VICReg.
result Computer vision SSL frameworks can be effective on time series data.
The UCR Time Series Archive - introduced in 2002, has become an important resource in the time series data mining community, with at least one thousand published papers making use of at least one data set from the archive. The original incarnation of the archive had sixteen data sets but since that time, it has gone th…
Research into the classification of time series has made enormous progress in the last decade. The UCR time series archive has played a significant role in challenging and guiding the development of new learners for time series classification. The largest dataset in the UCR archive holds 10 thousand time series only; w…
Benchmark study evaluates 8 clustering methods on 99 UCR time series datasets.
problem Assessing the performance of clustering methods on time series data.
method Examines 8 clustering methods across 3 categories and 3 distance measures on 99 UCR datasets.
result Provides a comprehensive dataset-level assessment of clustering methods.
Recent anomaly detection benchmarks are flawed, potentially misleading progress.
problem Flawed benchmark datasets create misleading progress reports.
method Identified four flaws in benchmark datasets and introduced a new archive.
result Published comparisons may be unreliable due to flaws in benchmark datasets.
Adaptive weighting schemes enhance time-series data augmentation for financial and UCR datasets.
problem Limited size of time-series datasets hinders model performance.
method Two adaptive weighting schemes for automatic data augmentation.
result Improves annualized returns by over 50% on financial dataset and outperforms state-of-the-art on half of UCR datasets.
Time Series Classification (TSC) is an important and challenging problem in data mining. With the increase of time series data availability, hundreds of TSC algorithms have been proposed. Among these methods, only a few have considered Deep Neural Networks (DNNs) to perform this task. This is surprising as deep learnin…
Disjoint or Overlapping? Inference Windowing for Reconstruction-Based Time Series Anomaly Detectioncs.LG
New study on time series anomaly detection shows overlapping inference improves performance.
problem Heterogeneous evaluation practices and inference procedures in time series anomaly detection.
method Unified training, tuning, and evaluation protocol on TSB-AD benchmark, analyzing overlapping vs. disjoint inference.
result Overlapping inference yields consistent improvements, with average relative gain up to +28%.
There are now a broad range of time series classification (TSC) algorithms designed to exploit different representations of the data. These have been evaluated on a range of problems hosted at the UCR-UEA TSC Archive (www.timeseriesclassification.com), and there have been extensive comparative studies. However, our und…
A new method extracts features from time series data using iterated sums and improves classification accuracy.
problem Time series classification challenges.
method Feature extraction using iterated-sums signature (ISS) followed by a linear classifier.
result Competitive with state-of-the-art methods on UCR archive.
New algorithms benchmarked for multivariate time series classification.
problem Comparing algorithms for multivariate time series classification.
method Review and comparison of recent MTSC algorithms using the UEA archive.
result HIVE-COTE ensemble is most accurate for MTSC, but dynamic time warping is competitive.
Paper proposes a robust time series classification method using ResNet and Recurrence Plots.
problem Classifying time series data is challenging and underexplored.
method Transfer learning in Deep Neural Networks, 2D Recurrence Plots, ResNet architecture, simplified preprocessing.
result First time multi-time series classification using a single network.
CONFETTI improves interpretability of deep learning models for MTS by providing counterfactual explanations.
problem Lack of transparency in deep learning models for multivariate time series classification.
method CONFETTI is a novel multi-objective counterfactual explanation method that balances prediction confidence, proximity, and sparsity.
result CONFETTI outperforms state-of-the-art methods in various metrics, improving interpretability and decision support.
RED CoMETS improves multivariate time series classification accuracy.
problem Complexity of multivariate time series classification.
method Ensemble classifier RED CoMETS for symbolically represented multivariate time series.
result RED CoMETS achieves highest reported accuracy on 'HandMovementDirection' dataset.
Paper proposes transforming ATN to attack multivariate time series models.
problem Generating adversarial samples for multivariate time series classification models.
method Proposes using a distilled model as a surrogate to mimic attacked models and applies 1-NN DTW and FCN attacks.
result Both models were susceptible to attacks on all 18 datasets.
TimeVQVAE-AD detects anomalies in time series data with high accuracy and provides explainable results.
problem Detecting and explaining anomalies in time series data accurately.
method Masked latent generative modeling in time-frequency domain.
result TimeVQVAE-AD outperforms existing methods in anomaly detection and explainability.
Transfer learning for deep neural networks is the process of first training a base network on a source dataset, and then transferring the learned features (the network's weights) to a second network to be trained on a target dataset. This idea has been shown to improve deep neural network's generalization capabilities …
MINIROCKET speeds up time series classification by 75x.
problem High computational complexity in time series classification.
method Random convolutional kernels and linear classifier.
result Up to 75 times faster on larger datasets.
Enhanced Sampling Scheme improves masked generative modeling.
problem Limitations of existing sampling schemes in masked non-autoregressive generative modeling.
method ESS consists of three stages: Naive Iterative Decoding, Critical Reverse Sampling, and Critical Resampling.
result ESS achieves significant performance gains in unconditional and class-conditional sampling.
Time Series Data Augmentation for Neural Networks by Time Warping with a Discriminative Teachercs.LG
Guided warping augments time series data by aligning features with a teacher.
problem Small time series datasets limit neural network performance.
method Guided warping with a discriminative teacher to augment data deterministically.
result Significant improvement in performance on various time series datasets.
TimeVQVAE uses VQ for better time series generation.
problem Training GANs and RNNs for time series generation have limitations.
method Vector quantization with bidirectional transformer priors in time-frequency domains.
result Generates high-quality synthetic signals with better temporal consistency.
RST improves environmental time series classification accuracy using randomized B-spline trees.
problem Improving accuracy in classifying complex environmental time series.
method Randomized Spline Trees (RST) integrates randomized functional representations into ensemble learning.
result RST variants outperform standard Random Forests and Gradient Boosting on most environmental time series datasets.
Archive of 20 time series datasets for forecasting evaluation.
problem Lack of comprehensive time series forecasting datasets.
method Compilation and characterisation of 20 datasets from various domains.
result Characterisation and performance evaluation of datasets.
LETS-GZSL tackles GZSL for time series classification, achieving high accuracy.
problem Recognizing unseen classes from time series data when only seen examples are labeled.
method Embedding-based approach combined with attribute vectors.
result Achieves a harmonic mean of at least 55% on most UCR datasets.
Paper proposes DTW-SOM for visual exploration of time-series motifs.
problem Exploring motifs extracted by time-series motif discovery algorithms.
method Adapted Self-Organizing Map (DTW-SOM) using Dynamic Time Warping distance.
result DTW-SOM effectively visualizes motifs from time-series data.
End-to-end model for time series classification with missing data.
problem Time series classification with missing data.
method End-to-end neural network that unifies imputation and representation learning.
result Proposed model outperforms state-of-the-art approaches for incomplete time series classification.
Time series clustering is the process of grouping time series with respect to their similarity or characteristics. Previous approaches usually combine a specific distance measure for time series and a standard clustering method. However, these approaches do not take the similarity of the different subsequences of each …
sktime toolkit benchmarks time series classification algorithms for correctness and efficiency.
problem Benchmarking correctness and efficiency of time series classification algorithms.
method Implementation and comparison of six classifiers in sktime with their tsml equivalents.
result Significant differences in accuracy and efficiency between algorithms, with one causing debugging issues.
Closing the Gap Between Synthetic and Ground Truth Time Series Distributions via Neural Mappingcs.LG
NM-VQTSG improves synthetic time series fidelity by aligning distributions.
problem Fidelity challenges in VQ-based time series generation.
method Neural mapping model using U-Net to refine synthetic data.
result Significant improvements in FID, IS, and conditional FID metrics.
Improved scalability of BOSS ensemble for time series classification.
problem Non-trivial scalability issues in BOSS and WEASEL.
method Random selection of classifiers, ensembling techniques, and build time reduction.
result Significant reduction in build time with minimal accuracy loss.
Meta-learning improves few-shot time series classification.
problem Few labeled data for time series classification.
method Gradient-based meta-learning for residual neural networks.
result Meta-learning outperforms baselines on 41 datasets.
An emended and improved version of the present paper has been archived in math-ph/0505057, and a preliminary account of its content has been published in Phys.Rev.Lett. 92, 60601, (2004). Moreover, in order to prove the relevance of topology for phase transition phenomena in a broad domain of physically interesting cas…
A technique called 'prior laundering' uses legacy reconstructions to create uncertainty in Bayesian inverse problems.
problem Uncertainty in Bayesian inverse problems when data is uninformative.
method Using an archive of legacy reconstructions to create uncertainty in the posterior distribution, averaging the legacy posterior over measurements.
result The uncertainty reported in the posterior is inherited from the legacy reconstructions, not from the data itself.
TS-CHIEF improves TSC accuracy while drastically reducing runtime.
problem Infeasibility of HIVE-COTE for large datasets.
method Combines time series embeddings into a tree-structured classifier.
result Achieves state-of-the-art accuracy with 130k time series in 2 days.
Co-eye: A Multi-resolution Symbolic Representation to TimeSeries Diversified Ensemble Classificationcs.LG
Co-eye combines multiple symbolic representations to improve time series classification accuracy.
problem Challenges in time series classification due to domain diversity.
method Inspired by compound eyes, Co-eye uses multiple symbolic representations and hyper-parameterised lenses to classify time series data.
result Co-eye outperforms state-of-the-art techniques in accuracy and robustness across various domains.
New metrics fail adversarial tests, with some more robust than others.
problem Evaluation metrics for time-series anomaly detection were improved but not fully robust.
method Adversarial stress-testing of 12 adopted metrics on real benchmarks.
result Some metrics are more robust than others, with ROC-based metrics being gamed more often.
VOLARE provides standardized realized volatility measures from financial data.
problem Lack of standardized realized volatility measures from ultra-high-frequency data.
method Asset-specific pipeline for cleaning and sampling data, providing a wide range of realized estimators.
result Comprehensive set of realized estimators for equities, exchange rates, and futures.
XEM improves multivariate time series classification with explainable models.
problem Multivariate time series classification challenges.
method Hybrid ensemble method combining explicit boosting-bagging and implicit divide-and-conquer.
result XEM outperforms state-of-the-art MTS classifiers on public datasets.
Extends Popularity Bias Memorization theorem to new conditions.
problem Estimating alignment with top-k singular hyperspace.
method Extending theorem to arbitrary degree distributions and proving upper and lower bounds.
result Upper and lower estimates for alignment with top-k singular hyperspace.
A new method sorts models to find the best one with minimal risk.
problem Finding the best model with minimal risk among nested models.
method Nested Empirical Risk (NER) and Sorted NER (S-NER) methods.
result The S-NER method selects the true model order and the most parsimonious model.
Polymarket-v1 Database tracks 1.2B trades across 1.3M markets with 100% ground-truth direction.
problem Lack of ground-truth data in prediction markets archives.
method Ground-truth archive of 1.2B trades from Polymarket's CTF Exchange.
result Ground-truth data reveals systematic errors in microstructure metrics.
Describes automorphism group of Rauzy diagrams.
problem Understanding the structure of Rauzy diagrams.
method Using an example from Yoccoz's unpublished notes.
result Automorphism group described as a subgroup of symmetric group.
This is an review on the point classification of second order ODE's by Ruslan Sharipov. His works were published in 1997-1998 at the Electronic Archive at LANL and undeservedly forgotten. Last chapter is an application of this classification to the investigation of Painleve equations.
The paper critiques current time series classification evaluation methods.
problem Current performance evaluation methods in time series classification are criticized.
method No specific new method proposed, but a critical analysis of existing methods.
result Suggests a need for discussion and reflection on TSC performance evaluation.