A new method for embedding temporal relationships in graphs.
problem Limited performance of existing time-aware graph embedding methods.
method Integrates temporal smoothness and task-oriented negative sampling.
result Improves performance in various tasks, especially entity/relationship/temporal scoping prediction.
Capsule networks excel in understanding spatial relationships in 2D data for vision related tasks. Even though they are not designed to capture 1D temporal relationships, with TimeCaps we demonstrate that given the ability, capsule networks excel in understanding temporal relationships. To this end, we generate capsule…
Paper uses HGNN to predict stock types from relationships and temporal data.
problem Predicting stock types from complex market data.
method Integrates stock relationships and temporal data using HGNN.
result Effective prediction of stock types with HGNN model.
TTERGM models improve social network predictions by incorporating triadic relationships.
problem Lack of models capturing triadic relationships and social learning theories in temporal network data.
method Introduced TTERGM, a generative model that includes triadic relationships and social learning theory as additional probability distributions. Parameters are estimated via Monte Carlo maximum likelihood.
result TTERGM achieves improved accuracy and fidelity compared to existing models on social network data.
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.
ReGENN improves time series forecasting by considering inter and intra-temporal relationships.
problem Achieving reliable predictions in real-world time series applications.
method ReGENN combines graph evolution with deep recurrent learning to model dynamic dependencies among multiple variables.
result Sound improvement of up to 64.87% over competing algorithms in time-series forecasting.
Deep learning model predicts wind-wave relationship.
problem Characterize ocean wave climate for engineering applications.
method Two-stage deep learning model: CNN for spatial features, LSTM for temporal dependencies.
result Predicts spatio-temporal relationship between wind and significant wave height.
TAGM models time-varying connections between variables.
problem Inferring temporal relationships between covariates.
method Time Adaptive Gaussian Model (TAGM) using Hidden Markov Models and Gaussian Graphical Models.
result TAGM outperforms state-of-the-art methods for temporal network inference.
New model infers causal relationships from spatio-temporal data, even with unobserved confounders.
problem Challenges in inferring causal relationships from spatio-temporal data due to unobserved confounders.
method Spatio-Temporal Hierarchical Causal Models (ST-HCMs) that extend hierarchical causal modeling to the spatio-temporal domain, using the Spatio-Temporal Collapse Theorem.
result Validated the effectiveness of ST-HCMs on both synthetic and real-world datasets, demonstrating robust causal inference in complex dynamic systems.
Comorbid diseases co-occur and progress via complex temporal patterns that vary among individuals. In electronic health records we can observe the different diseases a patient has, but can only infer the temporal relationship between each co-morbid condition. Learning such temporal patterns from event data is crucial f…
OracleAD detects multivariate time series anomalies without labels.
problem Rare and unlabeled multivariate time series anomalies.
method OracleAD encodes past sequences into causal embeddings, projects them into a latent space, and identifies anomalies based on deviations from a stable latent structure.
result OracleAD achieves state-of-the-art results and is interpretable.
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.
Study on memory effects in RNNs learning temporal data.
problem Understanding memory effects in RNNs for temporal data learning.
method Mathematical analysis of continuous-time linear RNNs, focusing on approximation and optimization dynamics.
result Long-term memory requires a large number of neurons and slows down training.
Modeling complex systems with multi-resolution data and causal dependencies.
problem Accurate prediction of complex systems with varying causal dependencies and multi-resolution data.
method Score-based Variational Graphical Diffusion Model (Temporal-SVGDM) that constructs individual SDEs for each variable at its native resolution and couples them through a causal score mechanism.
result Improved prediction accuracy and causal understanding compared to existing methods, especially in temporal scenarios.
FS-GCLSTM predicts stock returns by leveraging value-chain relationships.
problem Traditional time series models fail to capture complex interdependencies in modern markets.
method FS-GCLSTM integrates value-chain networks and graph convolutions to predict stock returns.
result FS-GCLSTM consistently delivers superior portfolio performance compared to traditional models.
ESN model helps understand climate event impacts.
problem Understanding complex climate event impacts.
method Feature importance methods for ESNs on spatio-temporal climate data.
result Characterized relationships between Mount Pinatubo eruption variables.
Improved topic modeling captures temporal relationships in speech.
problem Lack of temporal information in LDA for speech analysis.
method Temporal Markov chain extension to LDA for acoustic unit discovery.
result Improved phone segmentation results compared to base LDA.
This research unifies concepts of fading memory in RNNs.
problem Unclear relationships between fading memory concepts in RNNs.
method Unified language and new proofs for fading memory concepts.
result Clarified relationships between fading memory concepts.
Spacetimeformer learns spatiotemporal relationships from data alone.
problem Forecasting multivariate time series with distinct spatial relationships.
method Transformers with dynamic graph connections learning interactions between space, time, and value.
result Competitive results on various time series prediction benchmarks.
Develops a hybrid deep learning model for stock price prediction.
problem Predicting daily stock prices in the stock market.
method Representation learning with Stock2Vec embedding and temporal convolutional layers.
result Achieves better performance on stock price prediction than benchmarks.
Proposes LSR-IGRU for improved stock trend prediction.
problem Challenges in stock price prediction due to complex relationships and nonlinear dynamics.
method Long short-term relationships matrix and improved GRU input for better temporal and relationship integration.
result Significantly improved accuracy in predicting stock trend changes.
Model predicts future term connections in biomedical research.
problem Capturing temporal dynamics and unobserved connections in biomedical term relationships.
method Variational inference model for positive-unlabeled learning on dynamic graphs.
result Model effectively predicts term relationships in real-world datasets.
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.
Long-term prediction of multivariate time series is still an important but challenging problem. The key to solve this problem is to capture the spatial correlations at the same time, the spatio-temporal relationships at different times and the long-term dependence of the temporal relationships between different series.…
Knowledge graphs (KGs) typically contain temporal facts indicating relationships among entities at different times. Due to their incompleteness, several approaches have been proposed to infer new facts for a KG based on the existing ones-a problem known as KG completion. KG embedding approaches have proved effective fo…
BSG learns dynamic network spillovers and uncertainty quantification.
problem Identifying indirect spillovers and systemic risk in dynamic networks.
method Bayesian Spillover Graphs using FEVD and Bayesian time series models.
result Significant performance gains over baselines in identifying source and sink nodes.
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.
A new tensor-based method for predicting temporal relationships in knowledge bases.
problem Predicting temporal relationships in evolving knowledge bases.
method Tensor decomposition of order 4 with new regularization schemes.
result Achieves state-of-the-art performance in temporal link prediction.
Recurrent neural networks (RNNs) are nonlinear dynamical models commonly used in the machine learning and dynamical systems literature to represent complex dynamical or sequential relationships between variables. More recently, as deep learning models have become more common, RNNs have been used to forecast increasingl…
A new model DKMPP integrates covariates and uses an integration-free method for spatio-temporal point processes.
problem Training intractable deep spatio-temporal point processes with multimodal covariates.
method DKMPP uses a deep kernel to model complex relationships and an integration-free score matching method.
result DKMPP and score-based estimators outperform baseline models in spatio-temporal point processes.
We present a new approach to understanding credit relationships between commercial banks and quoted firms, and with this approach, examine the temporal change in the structure of the Japanese credit network from 1980 to 2005. At each year, the credit network is regarded as a weighted bipartite graph where edges corresp…
Paper proposes a new framework for SAD using GANs.
problem Speech Activity Detection (SAD) in diverse conditions.
method Joint learning with GANs and temporal discriminator.
result Framework outperforms state-of-the-art SAD approaches.
dCMF models evolving patterns in multiway data with temporal dynamics.
problem Capturing evolving patterns in multiway datasets with temporal dependencies.
method Time-aware coupled factorization model constrained by LDS structure.
result dCMF outperforms alternatives in capturing complex dynamics.
The paper introduces new metrics for evaluating generative models of behavior.
problem Lack of quantitative evaluation criteria for unsupervised behavior discovery.
method Proposed and investigated several metrics for generative models of behavior.
result The proposed metrics correspond with biologists' intuitions and allow for model evaluation and bias understanding.
This work studies the entity-wise topical behavior from massive network logs. Both the temporal and the spatial relationships of the behavior are explored with the learning architectures combing the recurrent neural network (RNN) and the convolutional neural network (CNN). To make the behavioral data appropriate for th…
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.
Rhino learns causal relationships from time series data with history-dependent noise.
problem Discovering causal relationships from time series data with non-linear relations, instantaneous effects, and history-dependent noise.
method Combines vector auto-regression, deep learning, and variational inference.
result Demonstrates better causal relationship discovery performance compared to baselines.
SiBBlInGS discovers interpretable building blocks across states in multi-way data.
problem Identifying interpretable units (Building Blocks) in multi-state, multi-way data.
method Graph-based dictionary learning approach for sparse BBs and temporal traces.
result Captures per-trial variability and state-specific vs. state-invariant components.
We describe a new framework for causal inference and its application to return time series. In this system, causal relationships are represented as logical formulas, allowing us to test arbitrarily complex hypotheses in a computationally efficient way. We simulate return time series using a common factor model, and sho…
Multi-horizon forecasting problems often contain a complex mix of inputs -- including static (i.e. time-invariant) covariates, known future inputs, and other exogenous time series that are only observed historically -- without any prior information on how they interact with the target. While several deep learning model…
Enhances understanding of patient healthcare journeys using self-attention.
problem Capturing hidden dependencies in multi-level patient journey data.
method Proposes a multi-level self-attention network (MusaNet) for encoding patient journeys.
result MusaNet produces higher-quality representations than state-of-the-art methods.
Unified model learns from both time-series and cross-sectional momentum features.
problem Separate time-series and cross-sectional momentum strategies do not consider concurrent relationships.
method Spatio-Temporal Momentum strategies using neural networks to combine both types of momentum.
result Simple neural network with single fully connected layer generates trading signals for all assets.
Information theory provides ideas for conceptualising information and measuring relationships between objects. It has found wide application in the sciences, but economics and finance have made surprisingly little use of it. We show that time series data can usefully be studied as information -- by noting the relations…
Meta-learning framework for credit risk assessment of SMEs, aligning financial statement dates with evaluation dates.
problem Temporal misalignment of credit scoring models leading to bias and inconsistent predictions.
method Two-step temporal decomposition: static model for annual PDs, dynamic model for monthly PDs; stacking architecture to aggregate multiple models.
result Framework effectively captures credit risk evolution over time, improving temporal consistency and predictive stability.
GATGPT uses LLMs with graph attention for spatiotemporal data imputation.
problem Missing values in spatiotemporal data due to sensor malfunctions and data transmission errors.
method Integrates pre-trained large language models with graph attention mechanisms.
result GATGPT achieves comparable results to deep learning benchmarks on real-world datasets.
Enhances reinforcement learning uncertainty estimation with a generalized Gaussian error model.
problem Inaccurate error representations and compromised uncertainty estimation in conventional uncertainty-aware TD learning.
method Introduces a novel framework for generalized Gaussian error modeling in deep reinforcement learning, incorporating higher-order moments, particularly kurtosis, to improve uncertainty estimation and mitigation.
result Significant performance gains in policy gradient algorithms with the proposed framework.
New protocol evaluates synthetic data for temporal consistency.
problem Synthetic data generators can produce invalid timestamps and trajectories.
method Characterize datasets by four properties, then measure timestamp validity and dynamics.
result Temporal fidelity must be measured, not inferred from static data.
Enhances thematic investing with stock embeddings from textual data.
problem Challenges in constructing thematic portfolios due to overlapping sector boundaries and evolving market dynamics.
method Introduces THEME, a framework that fine-tunes embeddings using hierarchical contrastive learning, aligning themes and stocks using their hierarchical relationship and incorporating stock returns.
result Theme-aligned portfolios demonstrate compelling performance, significantly outperforming large language models in thematic asset retrieval.