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.
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.
This paper presents a new methodology for clustering multivariate time series leveraging optimal transport between copulas. Copulas are used to encode both (i) intra-dependence of a multivariate time series, and (ii) inter-dependence between two time series. Then, optimal copula transport allows us to define two distan…
A new framework for generating predictive features in noisy multivariate time series.
problem Predicting noisy multivariate time series with limited user effort.
method Develops a feature programming framework based on spin-gas dynamical Ising models.
result Validated the method on synthetic and real-world datasets.
CATS enhances MTSF by generating ATS from OTS to improve forecasting accuracy.
problem Recent deep learning models often outperform multivariate ones in MTSF.
method CATS constructs ATS from OTS using a 2D temporal-contextual attention mechanism.
result CATS achieves state-of-the-art performance with reduced complexity.
Paper develops multivariate time series similarity and distance measures.
problem Compensating for misalignments in multivariate time series data.
method Adapted Independent and Dependent DTW strategies to seven elastic similarity and distance measures.
result Each measure achieves highest accuracy on at least one dataset, supporting their value.
A new framework detects anomalies in multivariate time-series data.
problem Lack of explicit relationships between time-series data.
method Graph Attention Network framework that considers temporal and feature dependencies.
result Outperforms state-of-the-art models on three real-world datasets.
Proposes a GNN for multivariate time-series prediction with filtering.
problem Low signal-to-noise ratio in complex systems data.
method Integrates a spatial-temporal GNN with a matrix filtering module to generate filtered graphs.
result Proposed model outperforms baseline approaches in multivariate time-series prediction.
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…
New framework assesses and benchmarks ML methods for multivariate time series.
problem Benchmarking and explaining performance of machine learning methods.
method Proposes a new framework with systematized performance-explainability characteristics.
result Illustrates application to multivariate time series classifiers.
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.
Develops a method for multivariate time series prediction intervals.
problem Uncertainty quantification in multivariate time series forecasting.
method Conformal prediction method for multivariate time series.
result Empirically demonstrates valid coverage of prediction regions.
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…
Kernel-based tests detect dependencies in multivariate time series, including stationary and non-stationary data.
problem Detecting dependencies in multivariate time series data, especially non-stationary data.
method Kernel-based statistical tests of joint independence, extending dHSIC to handle both stationary and non-stationary processes.
result Robustly uncovers significant higher-order dependencies in synthetic and real-world data.
AdaPTS adapts univariate FMs for multivariate time series forecasting.
problem Challenges in managing feature dependencies and uncertainty quantification in multivariate time series forecasting.
method Adapters that transform multivariate inputs into a latent space and apply univariate FMs independently to each dimension.
result AdaPTS enhances forecasting accuracy and uncertainty quantification compared to baseline methods.
LAVARNET predicts multivariate time series by estimating causal variable relationships.
problem Forecasting multivariate time series requires understanding causal interrelationships among variables.
method LAVARNET is a neural network architecture that estimates causal effects and predicts future values.
result LAVARNET outperforms other models on various real-world data sets.
ScoreGrad predicts multivariate time series with energy-based models, achieving state-of-the-art results.
problem Predicting multivariate time series with generative models while considering noise and distribution.
method ScoreGrad uses continuous energy-based generative models with a feature extraction and score matching module.
result ScoreGrad achieves state-of-the-art results on six real-world datasets.
Proposes a GNN framework for multivariate time series forecasting.
problem Lack of exploiting latent spatial dependencies in multivariate time series forecasting.
method Automatically extracts graph structures from multivariate time series data, integrates external knowledge, and uses mix-hop and dilated inception layers for capturing dependencies.
result Outperforms state-of-the-art methods on 3 out of 4 benchmark datasets.
Paper proposes counterfactual explanations for ML on multivariate time series data.
problem Lack of user trust and difficulty in debugging ML frameworks using multivariate time series data.
method Proposes a novel explainability technique for providing counterfactual explanations.
result Outperforms state-of-the-art explainability methods in metrics like faithfulness and robustness.
Proposes a new model for online anomaly detection in multivariate time series.
problem Inaccurate anomaly detection in multivariate time series due to spurious correlations and lack of temporal causality.
method Clusters channels based on correlations, embeds each cluster, and integrates information through a causal mixer while maintaining temporal causality.
result Consistently superior performance across six public benchmark datasets.
MTSCI uses diffusion models to impute multivariate time series data with consistency.
problem Imputation of missing values in multivariate time series data.
method MTSCI employs a contrastive complementary mask and mixup mechanism to ensure intra-consistency and inter-consistency.
result MTSCI achieves state-of-the-art performance on multivariate time series imputation tasks.
Proposes iVDFM for identifying latent factors in multivariate time series.
problem Identifying latent factors in multivariate time series with structural dynamics.
method Identifiable Variational Dynamic Factor Model (iVDFM) with iVAE-style conditioning.
result Identifiable latent factors up to permutation and component-wise affine transformations.
This paper extends hypergraph construction to multivariate time series using signature transforms.
problem Constructing hypergraphs from collections of multivariate time series.
method Leveraging signature transforms to introduce controlled randomness and robustness.
result Validated on synthetic datasets, the method enhances robustness in hypergraph construction.
InGRA models for efficient Granger causality learning in multivariate time series.
problem Efficiently modeling Granger causality in large-scale multivariate time series data.
method Inductive GRanger causal modeling (InGRA) framework with prototypical Granger causal attention.
result InGRA detects common causal structures and infers Granger causal structures for new individuals.
OFTER predicts multivariate time series online, outperforming baselines.
problem Mid-sized multivariate time series forecasting challenges.
method k-nearest neighbors, Generalized Regression Neural Networks, dimensionality reduction.
result OFTER outperforms state-of-the-art baselines in financial multivariate time series forecasting.
MFSSA improves reconstruction accuracy of multivariate functional time series.
problem Improving reconstruction accuracy of multivariate functional time series.
method Developed MFSSA, a functional extension of MSSA, for different dimensional domains.
result Better reconstruction accuracy of MFTS signals using MFSSA compared to other methods.
Multi-SpaCE generates valid counterfactual explanations for multivariate time series data.
problem Lack of transparency in deep learning models for multivariate time series data.
method Multi-objective counterfactual explanation method using NSGA-II for multivariate time series data.
result Ensures perfect validity and superior performance compared to existing methods.
Algorithm detects lead-lag relationships in multivariate time series.
problem Understanding temporal dependencies between time series.
method Cluster-driven methodology based on dynamic time warping.
result Robust detection of lead-lag relationships in lagged multi-factor models.
MTS-CycleGAN adapts multivariate time series data for ironmaking industry.
problem Creating a domain invariant dataset from multivariate time series data of different blast furnaces.
method Adversarial-based deep mapping learning network (CycleGAN) with LSTM-based AutoEncoder and discriminator.
result MTS-CycleGAN successfully translates multivariate time series data between different blast furnaces.
A novel non-supervised method detects anomalies in multivariate time series.
problem Detecting anomalies in multivariate time series data.
method Partitioning based on clustering of correlation coefficients.
result Significant improvement in anomaly detection performance.
A method for self-supervised learning of multivariate time series data across varying channels.
problem Labeling multivariate biomedical time series data is laborious and expensive.
method Proposes a multi-view self-supervised learning approach using a message passing neural network to extract a single representation across channels.
result Our method, combined with the TS2Vec loss, outperforms all other methods in most settings.
This paper evaluates anomaly detection methods for multivariate time series data.
problem Lack of systematic comparison of anomaly detection methods on multivariate time series data.
method Comprehensive evaluation of 10 models and 4 scoring functions on 10 datasets.
result Dynamic scoring functions outperform static ones, and the choice of scoring functions matters more than the model choice.
Proposes a model to detect changes in multivariate time series data.
problem Detect abrupt changes in multivariate time series data considering dependencies and correlations.
method Integrates graph neural networks into an encoder-decoder framework to model correlation structures and dynamics.
result Advantageous performance on CPD tasks over strong baselines, classifying changes as correlation or independent.
Paper introduces COBRA variations for multivariate time series forecasting.
problem Multivariate time series forecasting challenges.
method Innovative COBRA variations, data preprocessing, Bayesian optimisation vs. grid search.
result Proposed methodologies outperform state-of-the-art models.
A new model detects and localizes anomalies in multivariate time series data.
problem Anomaly diagnosis in multivariate time series data, especially localization.
method Attention Low-Rank Transformer (ALoRa-T) with low-rank regularization and Attention Low-Rank score.
result The proposed method significantly outperforms state-of-the-art methods in anomaly detection and localization.
Improved model for multivariate time series prediction with simpler architecture.
problem Multivariate probabilistic time series prediction challenges.
method Simplified transformer-based attentional copulas (TACTiS) with linearly scalable parameters.
result Significantly better training dynamics and state-of-the-art performance.
DUET enhances multivariate time series forecasting by clustering time and channels.
problem Heterogeneous temporal patterns and complex channel correlations in multivariate time series.
method DUET uses dual clustering on temporal and channel dimensions to handle these challenges.
result DUET achieves state-of-the-art performance on 25 real-world datasets.
MTHetGNN models complex relations in multivariate time series forecasting.
problem Complex relations among variables in multivariate time series forecasting.
method Designs a relation embedding module and a temporal embedding module, using graph neural networks and CNNs.
result Achieves state-of-the-art results in multivariate time series forecasting.
A real-world dataset is provided from a pulp-and-paper manufacturing industry. The dataset comes from a multivariate time series process. The data contains a rare event of paper break that commonly occurs in the industry. The data contains sensor readings at regular time-intervals (x's) and the event label (y). The pri…
This work proposes a method to learn graph structure for multivariate time series forecasting.
problem Improving multivariate time series forecasting by leveraging pairwise information.
method Learning a probabilistic graph model through optimizing mean performance over graph distribution parameterized by a neural network.
result Our method outperforms existing approaches in simplicity, efficiency, and performance.
Paper proposes LATC for multivariate time series prediction and missing data imputation.
problem Large-scale, incomplete, and corrupted multivariate time series data.
method Transforms multivariate time series into a tensor structure, models global and local trends, and uses autoregressive norm.
result Integration of global and local trends improves missing data imputation and rolling prediction.
Bayesian model clusters brain activity time series.
problem Heterogeneous multivariate time series in brain imaging.
method Group-based Bayesian mixture of smoothing splines with covariate effects.
result Distinct brain activity patterns identified.
mGRN improves multivariate time series prediction by managing marginal and joint memories.
problem Extracting dependencies in multivariate sequential data with strong serial and cross-sectional dependencies.
method Developed a novel recurrent network architecture, Memory-Gated Recurrent Networks (mGRN), with gates for marginal and joint memories.
result mGRN consistently outperforms state-of-the-art architectures on various public datasets.
CoCAI uses copulas for accurate multivariate time-series forecasting and anomaly detection.
problem Accurate multivariate time-series forecasting and robust anomaly detection.
method Copula-based conformal prediction for multivariate time-series analysis.
result CoCAI provides statistically valid predictive regions and robust anomaly scores.
New method explains anomalies in multivariate time series data.
problem Understanding and explaining anomalies in multivariate time series data.
method Counterfactual reasoning applied to MDI-detected anomalous intervals.
result Our method accurately identifies and explains anomalies in various extreme events.
ARM improves multivariate time series forecasting by better capturing series-wise relationships.
problem Challenges in handling complex temporal-contextual relationships in multivariate time series forecasting.
method ARM is an enhanced multivariate LTSF architecture that employs Adaptive Univariate Effect Learning, Random Dropping, and Multi-kernel Local Smoothing.
result ARM outperforms vanilla Transformers on multiple benchmarks without significantly increasing computational costs.
This study considers the multivariate segmentation procedure under the assumption of the multivariate Gaussian mixture. Jensen-Shannon divergence between two multivariate Gaussian distributions is employed as a discriminator and a recursive segmentation procedure is proposed. The daily log-return time series for 30 cur…
STAM learns important time steps and variables for multivariate time series prediction.
problem Accurate interpretation of multivariate time series predictions.
method Spatiotemporal attention mechanism (STAM) for multivariate time series modeling.
result STAM maintains state-of-the-art prediction accuracy with improved interpretability.