ExpCLR uses expert features to improve time-series representation learning.
problem Current representation learning approaches fail to ensure useful properties for time-series data.
method ExpCLR employs expert features to replace data transformations in contrastive learning, ensuring two useful properties for time-series representations.
result ExpCLR outperforms state-of-the-art methods on three real-world time-series datasets.
We found a new series to calculate Black-Scholes efficiently.
problem Calculating the Black-Scholes formula efficiently.
method Proved and tested a series representation for the European Black-Scholes call.
result Works in every market configuration and refines previous approximations.
VQShape learns interpretable time-series representations and achieves comparable performance to specialist models.
problem Lack of interpretability in existing time-series models.
method Vector quantization of time-series data into abstracted shapes.
result VQShape achieves comparable performance to specialist models in classification tasks.
SOM-VAE learns interpretable discrete time series representations.
problem Difficult interpretation of high-dimensional time series representations.
method Interpretable discrete representation learning framework combining self-organizing maps and variational autoencoders.
result Smooth and interpretable embeddings with superior clustering performance.
Proposes a new approach to time series representation learning by embedding patches independently.
problem Capturing dependencies between time series patches is not optimal for representation learning.
method 1) Patch reconstruction task, 2) Patch-wise MLP, 3) Complementary contrastive learning.
result Improves time series forecasting and classification performance compared to state-of-the-art models.
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.
ABBA creates a new symbolic time series representation based on Brownian bridge.
problem Representing time series data in a compact, symbolic form.
method Adaptive polygonal chain approximation followed by mean-based clustering.
result ABBA outperforms other representations in preserving time series shape information.
SoftCLT improves time series representation learning by soft contrastive loss.
problem Ignoring inherent correlations in time series leads to poor representation quality.
method SoftCLT introduces instance-wise and temporal contrastive loss with soft assignments.
result SoftCLT consistently improves various downstream tasks in time series learning.
A new unsupervised contrastive learning framework improves time series representation learning.
problem Lack of labeled data in time series data.
method Proposes an unsupervised contrastive learning framework using a novel contrastive loss and data augmentation.
result Framework outperforms other approaches on univariate and multivariate time series, and benefits transfer learning.
The paper presents a series representation for European option pricing driven by fractional diffusion.
problem Pricing European options under space-time fractional diffusion.
method Uses Mellin-Barnes representation and residue summation in the complex plane.
result Derives a rapidly convergent double-series formula for option pricing.
MDF represents time series motifs as images for improved classification.
problem Classifying time series data with high-order patterns.
method Motif Difference Field (MDF) using Fully Convolutional Networks (FCN).
result MDF outperforms other methods on UCR time series datasets.
Paper introduces TS-GPT for engineering time series forecasting.
problem Engineering time series require causal operations, unlike linguistic data.
method Innovations representation theory, Generative Pre-trained Transformer.
result TS-GPT effectively forecasts real-time locational marginal prices.
TNC learns time series representations by leveraging temporal neighborhoods.
problem Complex, unlabeled time series data.
method Temporal Neighborhood Coding (TNC) with a debiased contrastive objective.
result TNC outperforms other unsupervised methods in time series clustering and classification.
Symbolic LSTM improves time series forecasting by reducing hyperparameter sensitivity.
problem High sensitivity to hyperparameters and random initialization in numerical time series forecasting.
method Combining LSTM with a dimension-reducing symbolic representation.
result Symbolic representation alleviates forecasting problems and speeds up training.
Paper proposes a unified time series forecasting model with adaptive transfer.
problem General forecasting models for diverse time series data.
method Unified representations through Decomposed Frequency Learning and adaptive domain-specific features via Time Series Register.
result State-of-the-art forecasting performance on seven real-world benchmarks.
We sharpen the construction of representation space in the paper "Principal Series Representations of Infinite Dimensional Lie Groups II: Construction of Induced Representations". We show that the principal series representation spaces constructed there, are completions of spaces of sections of Hilbert bundles rather t…
MuSiCNet tackles irregularly sampled multivariate time series by treating them as a hierarchy of relatively regular series.
problem Irregularly sampled multivariate time series with missing values.
method Gradual coarse-to-fine approach with multi-scale and multi-correlation attention network.
result MuSiCNet improves ISMTS representation quality through hierarchical learning.
Unsupervised method learns universal embeddings for variable-length multivariate time series.
problem Challenges in learning representations for time series data due to varying lengths and sparse labeling.
method Combines causal dilated convolutions with triplet loss for time-based negative sampling.
result Demonstrates quality, transferability, and practicability of learned representations.
Meta-GLAR combines global deep representations with local adaptation for improved forecasting accuracy.
problem Joint learning from related time series boosts accuracy but fails for out-of-sample forecasting.
method Meta-GLAR uses a meta-learning approach to adapt RNN representations for each time series.
result Meta-GLAR outperforms state-of-the-art methods in out-of-sample forecasting accuracy.
Generative model improves time series prediction quality.
problem Lack of effective generative models for time series data.
method Proposed XIRP for time series, trained with WGAN-GP.
result XIRP significantly outperforms RNN-based models in predictive ability.
The paper classifies and proves properties of symmetry breaking operators for specific groups.
problem Classifying and understanding symmetry breaking operators for de Sitter and Lorentz groups.
method Constructing and classifying differential symmetry breaking operators, proving localness, and showing sporadic nature.
result All symmetry breaking operators are differential and sporadic, not obtainable by residue formulas.
The paper shows how to answer future and past questions from high-dimensional time series data.
problem Challenges in answering probabilistic inference questions from high-dimensional time series data.
method Temporal contrastive learning to learn Gaussian representations that enable compact closed-form solutions.
result Representations learned via contrastive learning follow a Gauss-Markov chain, enabling efficient inference and planning.
Proposes a parsimonious graph spectral method for time series data.
problem Efficiently transmitting multivariate time series data.
method Graph spectral embedding with unsupervised, parsimonious encoding.
result Near-linear computational complexity and interpretable event structure.
New framework learns blood sample MTS representations with missing data.
problem Missing data in clinical time series.
method Combines autoencoder with TCK kernel for missing data.
result Improved classification of blood samples with missing data.
RISE framework unifies and improves time series learning with missing data.
problem Learning from time series with missing data.
method RISE framework unifies and improves time series learning with missing data.
result RISE instances always benefit from encoders that learn representations for numerical values.
Universal neural network encoder learns from diverse time series data.
problem Learning representations for unseen data types in time series.
method Convolutional neural network with attention mechanism for compact representation.
result Adaptation strategies improve performance on unseen data types.
A novel unsupervised method detects anomalies in time series data robust to warping.
problem Detecting anomalies in time series data is challenging due to warping and lack of labeled data.
method WaRTEm-AD method operates in two stages: representation learning through autoencoders and anomaly detection on learned representations.
result WaRTEm-AD effectively detects both point and sequence anomalies in time series data.
Proposes a method to improve financial time series forecasting using compact representations and contrastive loss.
problem Financial time series forecasting with small datasets and overfitting issues.
method Class-conditioned latent variable model, mutual information maximization, contrastive loss, deep autoregressive models.
result Empirical experiments show improved performance compared to state-of-the-art methods.
Unified framework for series representations and finite approximations of CRMs.
problem Challenges in exact simulation and scalable inference with infinite-activity CRMs.
method Unified framework based on size-biased sampling of Poisson point process.
result Novel series representations for generalized gamma and stable beta processes.
New method learns low-dimensional representations of nonlinear time series without supervision.
problem Learning low-dimensional representations of nonlinear time series without supervision.
method Based on monotone variational inequality, the method learns representations by assuming sequences arise from a common domain.
result The method can learn the geometry for the entire domain and faithful representations for the dynamics of each individual sequence.
Transformer learns representations from time series data for money laundering detection.
problem Detecting money laundering using structured time series data.
method Contrastive learning for representation learning, followed by scoring and thresholding.
result Transformer outperforms rule-based and LSTM methods in detecting money laundering with controlled false positives.
Unsupervised learning improves clinical predictions from medical time series.
problem Improving clinical decision making through unlabeled medical data.
method Evaluation of unsupervised representation learning on medical time series using sequence-to-sequence models.
result A forecasting Seq2Seq model with an attention mechanism achieves the best performance.
Meta-learning for Koopman spectral analysis with short time-series data.
problem Lack of long time-series for training embedding functions in Koopman spectral analysis.
method Meta-learning approach using bidirectional LSTM and neural network to estimate embedding functions from short time-series.
result The proposed method achieves better performance in eigenvalue estimation and future prediction compared to existing methods.
PULSE learns representations from physiological time series.
problem Lack of effective pretraining objectives for physiological time series.
method Proposes a pretraining framework exploiting dynamical systems generative model.
result PULSE learns representations that distinguish semantic classes and improve transfer learning.
We consider exact sequences and lower central series of surface braid groups and we explain how they can prove to be useful for obtaining representations for surface braid groups. In particular, using a completely algebraic framework, we describe the notion of extension of a representation introduced and studied recent…
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.
RNF learns distinct representations for Bayesian filtering steps, improving time series prediction accuracy and uncertainty.
problem Improving time series prediction accuracy and uncertainty using distinct representations for Bayesian filtering steps.
method Introduces Recurrent Neural Filter (RNF) architecture that learns distinct representations for each Bayesian filtering step.
result RNF improves accuracy of one-step-ahead forecasts and provides realistic uncertainty estimates.
Adversarial regularization helps learn interpretable shapelets for time series classification.
problem Difficult to interpret learned shapelets in time series classification.
method Use of adversarial regularization to constrain model to learn more interpretable shapelets.
result Adversarially regularized method learns interpretable shapelets.
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.
Equivariant neural networks use symmetry to interpret complex data.
problem Interpreting and understanding the behavior of equivariant neural networks.
method Decompose layers into simple representations and analyze nonlinear activation functions.
result Equivariant neural networks can be interpreted using a filtration generalizing Fourier series.
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.
TimeAutoML learns effective representations for irregularly sampled MTS data without manual tuning.
problem Learning effective representations for multivariate time series with irregular sampling rates and variable lengths.
method Autonomous representation learning pipeline with negative sample generation and auxiliary classification task.
result TimeAutoML achieves up to 20% performance improvement in anomaly detection on UCR datasets.
This review connects knot invariants to quiver representations.
problem Relating knot invariants to quiver representations.
method Relates symmetric quivers and their partition functions to quantum invariants of knots.
result Establishes a correspondence between knot invariants and quiver representations.
For Poincare series of binary polyhedral groups and Coxeter polynomials there are obtained statements close to the Euclid algorithm and orthogonal polynomials theory: generalized Ebeling formula, decompositions into ramified continued fractions, Christoffel-Darboux identity, combinatorial formula. Known results about t…
TSLANet improves time series models by capturing long-term and short-term interactions.
problem Noise sensitivity, computational efficiency, and overfitting in Transformer-based models for time series data.
method Adaptive Spectral Block and Interactive Convolution Block for robust feature representation and noise mitigation.
result TSLANet outperforms state-of-the-art models in various time series tasks.
New algorithm speeds up model optimization for time series data.
problem Slow convergence in model optimization due to continuous symmetry.
method Gauge theory-inspired optimization algorithm.
result Orders of magnitude faster convergence and interpretable representations.
Model creates human-like text descriptions for time series data.
problem Creating textual summaries for complex time series data that mimic human behavior.
method Utility estimation model based on Bayesian network to rank patterns in time series data.
result Output is a natural language description of time series that matches human summary.
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.