TimeTrail detects financial fraud patterns through temporal correlation analysis.
problem Detecting and explaining complex financial fraud patterns.
method Temporal data enrichment, dynamic correlation analysis, interpretable pattern visualization.
result TimeTrail outperforms conventional methods in accuracy and interpretability.
Chimera model combines link, content, and time for dynamic network analysis.
problem Community detection and prediction in evolving networks with dynamic changes.
method Shared factorization model that accounts for graph links, content, and temporal analysis.
result The approach simplifies temporal analysis and enables future community prediction.
DMSTF models spatio-temporal data with deep Markov priors.
problem Analyzing nonlinear multimodal spatio-temporal dynamics.
method Deep Markov spatio-temporal factorization with stochastic variational inference.
result DMSTF outperforms other methods in predictive performance and clustering.
New analysis shows how temporal variability affects online learning performance.
problem Understanding the impact of temporal variability on online learning performance.
method Careful regret analysis and adaptive algorithm development.
result Proved a novel static regret bound that depends on temporal variability.
This study prioritizes temporal resolution over spatial in energy systems models due to higher influence.
problem The impact of spatial and temporal resolution on energy system models.
method Global sensitivity analysis to compare structural aspects, spatial, and temporal resolution.
result Temporal resolution has a higher influence on all results parameters compared to spatial resolution.
RTFN extracts robust temporal features for time series analysis.
problem Challenges in extracting sufficient shapelets from time series data.
method Combines temporal feature networks and attentional LSTM networks.
result RTFN outperforms in supervised and unsupervised time series analysis.
Generative model identifies temporal count data components with regime-dependent contributions.
problem Modeling temporal count data with regime-dependent dynamics.
method Generative framework combining regime-adaptive dynamics with Poisson log-normal emissions.
result Established identifiability of the model and revealed co-variation patterns and regime shifts.
Researchers analyze TD learning with linear approximations for efficiency.
problem Challenges in analyzing the statistical efficiency of TD learning.
method Finite time analysis using linear function approximation and stochastic gradient descent techniques.
result Simple and explicit finite time analysis of TD learning with linear function approximation.
Novel framework for spatio-temporal event analysis using Hawkes processes.
problem Inference of dynamics in spatio-temporal event sequences.
method Randomized Fourier feature-based transformations and gradient descent.
result Improved fitting capability in synthetic and real datasets.
FinTMMBench benchmarks RAG systems for finance tasks across multiple data types and time periods.
problem Evaluating temporal-aware multi-modal retrieval augmented generation in finance.
method TMMHybridRAG method that converts and integrates data from various modalities and temporal information.
result Demonstrated effectiveness of TMMHybridRAG in diverse financial analysis tasks.
Credit risk analysis improved with a joint model for spatial and temporal effects.
problem Predicting borrower's time-to-event with spatial and temporal covariates.
method Spatio-Temporal Joint Model (STJM) using Bayesian hierarchical approach and INLA.
result Spatial effects improve joint model performance, but spatio-temporal interactions have less impact.
OMBA learns product and user representations for better online market basket analysis.
problem Limited ability to uncover rarely occurring and temporal associations in MBA.
method Jointly learns product and user representations, captures temporal dynamics, scalable online method.
result OMBA outperforms state-of-the-art methods by 21% on real-world datasets.
New method clusters evolving networks using spatio-temporal graph Laplacian.
problem Clustering communities in time-varying graphs.
method Extends spectral clustering to dynamic graphs using CCA and spatio-temporal graph Laplacian.
result The spatio-temporal graph Laplacian clearly interprets cluster evolution over time.
Quantile Temporal-Difference learning proved convergent with proof.
problem Lack of theoretical understanding of QTD despite empirical success.
method Proof of convergence using stochastic approximation and non-smooth analysis.
result QTD converges to fixed points with probability 1.
Two-stream model recognizes affect from audio and video.
problem Human affect recognition in real-world settings.
method Two-stream aural-visual analysis model with separate audio and visual processing.
result Model achieves promising results on Aff-Wild2 database.
This letter extends the concept of graph-frequency to graph signals that evolve with time. Our goal is to generalize and, in fact, unify the familiar concepts from time- and graph-frequency analysis. To this end, we study a joint temporal and graph Fourier transform (JFT) and demonstrate its attractive properties. We b…
Gradient-based method extracts slow features from high-dimensional data.
problem Extracting meaningful low-dimensional features from high-dimensional, temporally varying data.
method Power Slow Feature Analysis (PowerSFA) using gradient-based training of differentiable architectures.
result PowerSFA effectively extracts meaningful low-dimensional features in various data types.
Convolutional LSTM improves missing data imputation in spatio-temporal data.
problem Missing data in spatio-temporal data affects analysis performance.
method Proposes a convolutional bidirectional-LSTM for spatio-temporal missing data imputation.
result The proposed model outperforms state-of-the-art methods for missing data imputation.
Study on eigenvalue distribution of correlated time series, showing deformation of Marchenko-Pastur distribution.
problem Eigenvalue distribution of Wishart matrix with temporal correlation.
method Analysis of moments and convergence to deformed Marchenko-Pastur distribution for Gaussian process with temporal correlation.
result Eigenvalue distribution converges to deformed Marchenko-Pastur distribution with longer tail and higher peak.
GNNs improve brain activity forecasting in fMRI studies.
problem Understanding neural dynamics in the brain.
method Comparison of GNN architectures for modeling fMRI data.
result GNNs outperform VAR models in robustly scaling to large network studies.
ConfusionFlow visualizes classifier confusion over time for model comparison.
problem Insufficient performance analysis of classifiers.
method Interactive, model-agnostic visualization tool combining confusion matrices and temporal analysis.
result ConfusionFlow facilitates detailed, comparative analysis of classifier performance over time.
TMSCD detects multi-scale communities in temporal networks automatically.
problem Discovering multi-scale communities in large, evolving networks.
method Spectral multilayer formulation of MM method with automatic parameter selection.
result Automatic detection of multi-scale communities without manual parameter selection.
Probabilistic Temporal Tensor Factorization (PTTF) is an effective algorithm to model the temporal tensor data. It leverages a time constraint to capture the evolving properties of tensor data. Nowadays the exploding dataset demands a large scale PTTF analysis, and a parallel solution is critical to accommodate the tre…
Temporal-difference and Q-learning learn feature representations that converge to optimal ones.
problem Understanding how feature representations evolve in temporal-difference and Q-learning with neural networks.
method Mean-field theory applied to overparameterized two-layer neural networks.
result The feature representation converges to the optimal one, generalizing previous results.
Digital currencies exhibit multifractality due to heavy-tailed returns and temporal correlations.
problem Understanding market inefficiencies and predicting volatility in digital currencies.
method Multifractal cross-correlation analysis (MFCCA) and multifractal detrended fluctuation analysis (MFDFA).
result Temporal correlations are the primary source of multifractality in digital currency markets.
Paper introduces a new reinforcement learning method with improved performance.
problem Designing and analyzing efficient reinforcement learning algorithms.
method Proximal gradient temporal difference learning (GTD) with accelerated algorithm GTD2-MP.
result GTD algorithms have linear complexity and improved convergence rate.
New method reconstructs networks from spatiotemporal data.
problem Network reconstruction from spatiotemporal data.
method Multivariate Hawkes processes using both temporal and spatial information.
result Spatiotemporal approach yields improved network reconstruction.
odeN efficiently approximates multiple temporal motifs in large networks.
problem Efficiently counting multiple temporal motifs in large temporal networks.
method odeN is a sampling-based algorithm that provides accurate probabilistic approximations of motif counts.
result odeN provides accurate approximations of motif counts in a fraction of the time needed by state-of-the-art methods.
Neural signals are characterized by rich temporal and spatiotemporal dynamics that reflect the organization of cortical networks. Theoretical research has shown how neural networks can operate at different dynamic ranges that correspond to specific types of information processing. Here we present a data analysis framew…
Paper improves video categorization using temporal coherence.
problem Video categorization in multiple modalities.
method Temporal coherence-based regularization for multimodal models.
result Models with temporal coherence outperform state-of-the-art.
Concept Relation Discovery and Innovation Enabling Technology (CORDIET), is a toolbox for gaining new knowledge from unstructured text data. At the core of CORDIET is the C-K theory which captures the essential elements of innovation. The tool uses Formal Concept Analysis (FCA), Emergent Self Organizing Maps (ESOM) and…
Spatio-temporal RBF neural networks improve chaotic time series prediction.
problem Predicting chaotic time series due to their dynamic nature.
method Proposes an spatio-temporal extension of RBF neural networks.
result Spatio-temporal RBF outperforms standard RBF in chaotic time series prediction.
FreST Loss decorrelates spatio-temporal dependencies in graph signals.
problem Complex spatio-temporal dependencies in graph-structured signals are not well captured by standard forecasting models.
method FreST Loss extends supervision to the joint spatio-temporal spectrum using Joint Fourier Transform (JFT).
result FreST Loss reduces estimation bias and improves forecasting accuracy on real-world datasets.
SWoTTeD discovers hidden temporal patterns in EHR data.
problem Complex temporal patterns in EHR data.
method Sliding Window for Temporal Tensor Decomposition (SWoTTeD) with constraints and regularizations.
result SWoTTeD achieves at least as accurate reconstruction as state-of-the-art models and extracts meaningful temporal phenotypes.
Video sequences contain rich dynamic patterns, such as dynamic texture patterns that exhibit stationarity in the temporal domain, and action patterns that are non-stationary in either spatial or temporal domain. We show that a spatial-temporal generative ConvNet can be used to model and synthesize dynamic patterns. The…
New clustering method for POIs from spatio-temporal data with temporal constraints.
problem Lack of temporal constraints in existing clustering methods for POIs.
method POI clustering with temporal constraints (PC-TC).
result PC-TC outperforms existing methods in next place prediction.
Improved TD learning with tail averaging and regularization achieves optimal convergence rates.
problem Convergence analysis of TD learning with linear function approximation.
method Tail-averaging and regularization applied to TD learning algorithm.
result Achieves optimal O(1/t) convergence rate in expectation and with high probability. The paper develops a new model to evaluate policies in complex temporal/spatial experiments.
problem Evaluating the impact of policies in experiments with temporal and spatial dependencies.
method Temporal/spatio-temporal Varying Coefficient Decision Process (VCDP) model, decomposing ATE into DE and IE.
result Effective estimation and inference of DE and IE with rigorous statistical analysis.
Transformer-based method for causal discovery with prior knowledge integration.
problem Complex nonlinear dependencies and spurious correlations in time series data.
method Multi-layer Transformer forecaster with gradient-based causal structure extraction and attention masking for prior knowledge integration.
result Significant improvement in causal discovery and causal lag estimation compared to state-of-the-art methods.
Framework reconstructs missing spatio-temporal data for extreme value prediction.
problem Predicting extreme values from incomplete spatio-temporal data.
method Convolutional deep neural networks and autoencoder-like models for conditional sampling.
result Framework produces accurate reconstructions of missing data for extremal values.
Detects malicious accounts in permissionless blockchains using graph properties and ML.
problem Identifying and classifying malicious accounts in permissionless blockchains.
method Temporal graph properties, ML algorithms (ExtraTreesClassifier, K-Means), cosine similarity, behavior change analysis.
result ExtraTreesClassifier performs best in detecting malicious accounts on Ethereum blockchain.
TGAT learns node embeddings for evolving graphs, capturing both static and temporal features.
problem Learning node embeddings for dynamic graphs with evolving topological structures and temporal patterns.
method Temporal Graph Attention (TGAT) layer using self-attention and functional time encoding.
result TGAT model can inductively infer node embeddings for new and observed nodes as the graph evolves.
Unified tensor factorization for efficient 3D convolutions in spatio-temporal emotion analysis.
problem Training deep 3D convolutions is computationally expensive and requires large datasets.
method Tensor factorization framework for separable higher-order convolutions.
result Improved spatio-temporal emotion estimation on large datasets.
T-CorEx efficiently estimates covariance from large temporal datasets.
problem Scalability and performance in high-dimensional undersampled regimes.
method Optimizes an information-theoretic objective function with temporal consistency regularization.
result State-of-the-art results in synthetic and real-world datasets.
Variant of mSSA improves time series prediction error.
problem Improve prediction error in multivariate time series.
method Introduce spatio-temporal factor model, establish prediction error scaling.
result Prediction error scales as 1 / √(min(N, T)T).
New method improves blockchain analysis by handling temporal changes and scalability.
problem Limited focus on evolving nature and scalability of blockchain transaction networks.
method Incremental approach with Metropolis-Hastings random walks.
result Comparable performance in node classification tasks with reduced computational overhead.
Functional brain networks exhibit dynamics on the sub-second temporal scale and are often assumed to embody the physiological substrate of cognitive processes. Here we analyse the temporal and spatial dynamics of these states, as measured by EEG, with a hidden Markov model and compare this approach to classical EEG mic…
Next-generation sequencing (NGS) to profile temporal changes in living systems is gaining more attention for deriving better insights into the underlying biological mechanisms compared to traditional static sequencing experiments. Nonetheless, the majority of existing statistical tools for analyzing NGS data lack the c…