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.
Paper introduces a new method for classifying interval-valued time series.
problem Classification of interval-valued time series.
method Extends point-valued time series imaging methods to interval-valued scenarios using DK-distance and employs deep learning for classification. result Proposed method achieves superior classification performance compared to existing methods.
Multivariate time series classification is a high value and well-known problem in machine learning community. Feature extraction is a main step in classification tasks. Traditional approaches employ hand-crafted features for classification while convolutional neural networks (CNN) are able to extract features automatic…
Research into time series classification has tended to focus on the case of series of uniform length. However, it is common for real-world time series data to have unequal lengths. Differing time series lengths may arise from a number of fundamentally different mechanisms. In this work, we identify and evaluate two cla…
A new robust time series distance metric for k-NN classification.
problem Robustness against arbitrary data contamination in time series classification.
method Proposes a novel distance metric with worst-case O(nlogn) complexity. result Demonstrates competitive classification accuracy in k-NN time series classification.
UniShape improves time series classification by selecting relevant subsequences.
problem Classifying time series data requires capturing interpretable shapelets.
method UniShape uses a shape-aware adapter to aggregate multiscale subsequences into class tokens.
result UniShape achieves state-of-the-art classification performance.
Efficient method classifies locally stationary time series based on second-order characteristics.
problem Classifying locally stationary time series for various applications.
method Autoregressive approximation, ensemble aggregation, distance-based threshold.
result Zero misclassification error rate asymptotically for mildly differing second-order characteristics.
Pattern sampling reduces time series classification complexity.
problem High computational complexity of exhaustive search for shapelets.
method Pattern sampling using a weighted trie to extract discriminative patterns.
result Significant reduction in computational and memory resources.
Study shows topological features improve time series classification.
problem Classifying stochastic processes with varying noise and sampling.
method Topological data analysis features compared to statistical and raw features.
result Topological features lead to better classification performance.
New method interprets multivariate time series for better results.
problem Difficulty in applying traditional methods to multivariate time series.
method Alternative representation of multivariate time series through features.
result Competitive and interpretable results achieved.
Over the past decade, multivariate time series classification has received great attention. We propose transforming the existing univariate time series classification models, the Long Short Term Memory Fully Convolutional Network (LSTM-FCN) and Attention LSTM-FCN (ALSTM-FCN), into a multivariate time series classificat…
Improved time series classification with imputed data using label-guided forest-based methods.
problem Missing data in time series data.
method Label-guided imputation using forest-based proximity measures.
result Imputation leads to higher classification accuracies, even with imputed values differing from true values.
Time series are series of values ordered by time. This kind of data can be found in many real world settings. Classifying time series is a difficult task and an active area of research. This paper investigates the use of transfer learning in Deep Neural Networks and a 2D representation of time series known as Recurrenc…
Paper proposes a new efficient transport-based dissimilarity measure for time series classification.
problem Classifying time series with warping distortions.
method Defining a problem statement, proposing an Optimal Transport-based dissimilarity measure.
result The proposed method can solve the time series classification problem with reduced computational cost.
New algorithm for uncertain time series classification.
problem Uncertainty in time series data.
method Uncertain dissimilarity measure based on Euclidean distance and uncertain shapelet transform.
result Effectiveness of the uncertain shapelet transform algorithm on state-of-the-art datasets.
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.
Simple mean and std-based classifier outperforms chance on 69 out of 128 time-series problems.
problem Time-series classification accuracy comparison
method Linear classifier using mean and standard deviation features
result Simple distributional features outperform chance on 69 out of 128 time-series problems
Proposes adaptive method for classifying interval-valued time series.
problem Lack of classification methods for interval-valued time series.
method Represent intervals as images, classify using CNN, optimize coefficients with ADMM.
result Validated through simulations and real data, outperforming point-valued methods.
Novel financial time-series data representation improves industry sector classification.
problem Classifying industries using historical stock returns time-series data.
method Proposed a novel representation based on stock returns embeddings for time-series data, overcoming representational challenges of conventional approaches.
result Substantial performance improvements over baselines using conventional representations.
This paper reviews early time series classification methods.
problem Minimizing class prediction delay in time-sensitive applications.
method Divided into four categories: prefix based, shapelet based, model based, and miscellaneous approaches.
result Demonstrates reasonable performance in various applications.
Classification of time series is a growing problem in different disciplines due to the progressive digitalization of the world. Currently, the state-of-the-art in time series classification is dominated by The Hierarchical Vote Collective of Transformation-based Ensembles. This algorithm is composed of several classifi…
Hybrid LSTM-fully convolutional networks (LSTM-FCN) for time series classification have produced state-of-the-art classification results on univariate time series. We show that replacing the LSTM with a gated recurrent unit (GRU) to create a GRU-fully convolutional network hybrid model (GRU-FCN) can offer even better p…
Survey of data augmentation techniques for time series classification with neural networks.
problem Small datasets in time series recognition.
method Four families of data augmentation: transformation-based, pattern mixing, generative models, and decomposition methods.
result Empirical evaluation of 12 data augmentation methods on 128 datasets.
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.
We show how binary classification methods developed to work on i.i.d. data can be used for solving statistical problems that are seemingly unrelated to classification and concern highly-dependent time series. Specifically, the problems of time-series clustering, homogeneity testing and the three-sample problem are addr…
Novel method converts time series data into functional data for high dimensional classification.
problem Small sample size problem in high dimensional time series data.
method Classwise Functional Principal Component Analysis (PCA) followed by Bayesian linear classifier.
result Demonstrated efficacy on synthetic and real data sets.
Paper introduces Native Guide for generating time series counterfactual explanations.
problem Lack of explainability for time series data in AI systems.
method Model-agnostic, instance-based counterfactual generation for time series classification.
result Native Guide produces better counterfactual explanations than benchmarks.
Capturing the dynamical properties of time series concisely as interpretable feature vectors can enable efficient clustering and classification for time-series applications across science and industry. Selecting an appropriate feature-based representation of time series for a given application can be achieved through s…
ALT improves TSC by capturing complex patterns in time series data.
problem Challenges in traditional TSC methods with time series complexity and variability.
method ALT incorporates variable-length shifted time windows to enhance LLT for better feature representation.
result ALT achieves state-of-the-art performance with few hyperparameters.
Time series classification models have been garnering significant importance in the research community. However, not much research has been done on generating adversarial samples for these models. These adversarial samples can become a security concern. In this paper, we propose utilizing an adversarial transformation …
r-STSF improves TSC accuracy and interpretability.
problem Lack of interpretability in state-of-the-art TSC methods.
method Randomized-Supervised Time Series Forest (r-STSF) using interval-based approach and ensemble of randomized trees.
result r-STSF achieves state-of-the-art accuracy and enables interpretability.
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.
ALT transforms time series data for better classification.
problem Efficiently classifying time series data with varying temporal scales.
method ALT algorithm using variable-length shifted time windows.
result State-of-the-art performance with minimal computational overhead.
Improves time series classification with forest proximities.
problem Time series classification accuracy and efficiency.
method PF-GAP, an extension of RF-GAP proximities to proximity forests, combined with Multi-Dimensional Scaling and Local Outlier Factors.
result Forest proximities show stronger connection between misclassified points and outliers.
Paper uses topological data analysis for time series classification.
problem Classifying univariate time series data, especially physiological signals.
method Persistent homology for feature engineering, followed by machine learning.
result Higher accuracy achieved with fewer features compared to traditional methods.
Paper tackles imbalanced time series classification with a novel oversampling method.
problem Imbalanced time series classification challenges due to high dimensionality and correlation.
method Density-ratio based clustering followed by shrinkage technique for covariance estimation, then generating synthetic samples.
result OHIT outperforms state-of-the-art methods in F1, G-mean, and AUC metrics.
Time series classification is an increasing research topic due to the vast amount of time series data that are being created over a wide variety of fields. The particularity of the data makes it a challenging task and different approaches have been taken, including the distance based approach. 1-NN has been a widely us…
Deep learning improves time series classification accuracy.
problem Classifying time series data efficiently.
method Developed deep neural networks for time series classification.
result Demonstrated superior performance of deep learning methods.
CaLoNet integrates spatial and local correlations for multivariate time series classification.
problem Ignoring spatial and local correlations in multivariate time series classification.
method Model spatial correlations using causality modeling, extract local correlations, integrate into graph neural network.
result Competitive performance compared to state-of-the-art methods on UEA datasets.
New model for time series classification from single example.
problem Classifying time series patterns from limited data.
method Developed a Hidden semi-Markov Model with variable state duration.
result Different representations of state duration have distinct strengths and weaknesses.
New linear models improve time series classification efficiency and interpretability.
problem Complex and inefficient classifiers limit interpretability and applicability to variable-length time series.
method Symbolic representations, multi-resolution, multi-domain, linear models.
result mtSS-SEQL+LR achieves similar accuracy to state-of-the-art methods but with lower time and memory usage.
New method explains time series classification by assessing causal effects.
problem Understanding machine learning model decisions in time series classification.
method Model-agnostic causal attribution method using diffusion models.
result Causal attributions differ from associational ones, highlighting risks.
Interpretable neural model for few-shot time-series classification.
problem Few-shot time-series classification challenges.
method Dual Prototypical Shapelet Networks (DPSN) framework.
result DPSN framework outperforms state-of-the-art methods, especially with limited data.
GeoStat simplifies time series classification with fast, intuitive features.
problem Efficiently classify time series data without high computational costs.
method GeoStat representations based on differential geometric statistics.
result Simple KNN and SVM classifiers achieve state-of-the-art performance.
New method classifies nonlinear time series using deep CNNs and bispectra.
problem Classifying nonlinear time series data effectively.
method Combines HOSA with deep CNNs.
result Effective classification of nonlinear time series data.
theft package simplifies feature extraction for time series analysis in R.
problem Lack of a unified access point and methodological pipelines for feature-based time series analysis.
method theft package provides a unified framework for computing features from six open-source time series feature sets.
result theft enables comprehensive quantification and interpretation of time series structure.
Paper tackles class-incremental time series classification with dual-stream feature extraction.
problem Class-incremental continual learning for multivariate time series data.
method Dual-stream feature extraction pipeline combining deep temporal embedding features and statistical features.
result Competitive average accuracy across multiple datasets with low forgetting rates.
CRITS improves time series classification with interpretable local explanations.
problem Lack of detailed explanations in time series classification models.
method CRITS uses convolutional kernels, max-pooling, and rectified linear units to extract feature weights.
result CRITS provides intrinsically interpretable local explanations without requiring gradients or random perturbations.