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.
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.
TIME explains temporal models by analyzing feature importance.
problem Existing methods struggle with temporal models and feature importance.
method Model-agnostic permutation-based approach, temporal feature importance, hypothesis testing.
result TIME provides statistical rigor for explaining temporal models.
Combines CNN and LSTM for spatio-temporal graph networks.
problem Improving spatio-temporal feature extraction.
method Proposes a new architecture combining CNN and LSTM temporal blocks.
result Empirical comparison shows our model outperforms existing models.
Model predicts severity of traffic accidents using spatial and temporal features.
problem Estimating severity of traffic accidents in aggregated and disaggregated data.
method Gradient Boosting models and Gaussian Processes for inference and feature importance.
result Complexity of road networks and other situational features significantly impact accident severity.
Enhances SNNs for spatio-temporal feature extraction.
problem Insufficient temporal dependencies in existing SNN synaptic structures.
method Integrates temporal convolution and attention mechanisms into synaptic connections.
result Improves SNN performance on classification tasks.
Bayesian framework extracts features from high-dimensional spatio-temporal data.
problem Sparse structure and spatio-temporal dependence in high-dimensional data.
method Develops a Bayesian feature-extraction framework using Gaussian and Diffused-gamma priors, employing Bregman divergence likelihood and MCMC for posterior computation.
result Improves recovery of sparse features and enhances interpretability in the presence of spatio-temporal dependence.
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.
FIBS extracts relevant features from IBTSs for classification.
problem Classifying interval-based temporal sequences (IBTSs) using common algorithms is challenging.
method FIBS extracts features from IBTSs based on relative frequency and temporal relations, incorporating a filter-based selection strategy to avoid irrelevant features.
result FIBS effectively represents IBTSs for classification algorithms, providing similar or better accuracy compared to state-of-the-art competitors.
Accurately predicting customer churn using large scale time-series data is a common problem facing many business domains. The creation of model features across various time windows for training and testing can be particularly challenging due to temporal issues common to time-series data. In this paper, we will explore …
Capsule networks improve temporal data understanding, achieving 96.21% ECG accuracy.
problem Improving temporal data understanding with capsule networks.
method Generated capsules along temporal and channel dimensions, learning contrasting relationships.
result Achieved 96.21% accuracy on ECG signal beat categories, surpassing state-of-the-art.
TSAM predicts directed temporal links using GCN and self-attention.
problem Predicting links in directed temporal networks.
method GCN, self-attention mechanism, autoencoder architecture, graph attentional layers, graph convolutional layers, graph recurrent unit layer.
result TSAM outperforms benchmarks on four realistic networks.
STAS selects optimal spatio-temporal scales for bias correction in precipitation forecasts.
problem Limited prior data and fixed ST scale in existing BCoPs lead to biases in numerical weather predictions.
method End-to-end deep-learning BCoP model STAS with SFM/TFM to automatically adjust spatial and temporal scales.
result STAS outperforms 8 published BCoP methods on threat scores (TS).
Hybrid model improves COVID-19 case forecasting accuracy.
problem Limited data and simplistic models for accurate prediction.
method Combining SEIR and RNN on a graph structure with local and edge features.
result Improves prediction accuracy on state-level COVID-19 data.
Unsupervised learning of time series data, also known as temporal clustering, is a challenging problem in machine learning. Here we propose a novel algorithm, Deep Temporal Clustering (DTC), to naturally integrate dimensionality reduction and temporal clustering into a single end-to-end learning framework, fully unsupe…
StrGNN detects anomalies in dynamic graphs by analyzing subgraphs and temporal features.
problem Detecting anomalies in dynamic graphs with structural changes.
method StrGNN is an end-to-end model that uses structural subgraphs and temporal features for anomaly detection.
result StrGNN effectively detects anomalies in dynamic graphs, as shown by extensive experiments.
HRTPP improves TPP interpretability and accuracy in medical event modeling.
problem Lack of interpretability in TPPs for medical event sequences.
method Hybrid-Rule Temporal Point Processes (HRTPP) integrating temporal logic rules and numerical features.
result HRTPP outperforms state-of-the-art interpretable TPPs in predictive performance and clinical interpretability.
New framework analyzes temporal features in state space models.
problem Understanding temporal dependencies in data streams.
method Proposes a framework for rigorous analysis of state representations in ESNs, using temporal feature spaces and kernel machines.
result Phase transition in kernel richness for cycle reservoir topology.
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.
Wind power prediction is of vital importance in wind power utilization. There have been a lot of researches based on the time series of the wind power or speed, but In fact, these time series cannot express the temporal and spatial changes of wind, which fundamentally hinders the advance of wind power prediction. In th…
SeqFM models dynamic and sequential features for better predictive analytics.
problem Inadequate handling of sequential dependencies in existing FM-based models.
method Introduces SeqFM, a novel model that incorporates multi-view self-attention to model static, dynamic, and their interactions.
result SeqFM outperforms existing models in ranking, classification, and regression tasks on six large-scale datasets.
This paper tackles spatio-temporal information preservation in machine learning.
problem Conventional machine learning assumes orthogonal data attributes, disrupting spatio-temporal information.
method Shift-invariant k-means, convolutional dictionary learning, and spatio-temporal hypercomplex encoding schemes are proposed.
result Gabor feature extraction outperforms convolutional dictionary learning in spatio-temporal information preservation.
IETNet identifies important channels for MVTS classification.
problem Multivariate time series classification with blackbox deep networks.
method End-to-end network combining temporal feature extraction, variable selection, and interaction.
result IETNet improves model accuracy and reduces overfitting by identifying and removing non-predictive variables.
HTGCN improves community detection in dynamic, heterogeneous graphs.
problem Challenges in detecting communities in graphs with varying features and temporal dynamics.
method Designs HTGCN combining heterogeneous GCN and residual compressed aggregation for dynamic feature representation.
result HTGCN outperforms existing methods on DBLP and IMDB datasets.
New framework for analyzing hydroclimatic time series across multiple scales.
problem Understanding geophysical processes and evaluating stochastic models across different time scales.
method A novel feature compilation method for multi-scale hydroclimatic analyses.
result Identified similarities and differences in time series types across various temporal resolutions.
A new method learns stable, temporally coherent features in point clouds.
problem Flickering and halo structures in inferred solutions for time-varying point clouds.
method Proposes a novel temporal loss function and super-resolution method.
result Demonstrates flexibility and effectiveness on large, deforming point sets.
Proposes a feature transformation for spatio-temporal traffic models to improve performance and transferability.
problem Limited transferability of deep learning models for traffic flow prediction across different locations.
method Integrates Newell's traffic flow estimators to capture broader dynamics and incorporates spatial dependencies.
result Improves model performance in predicting traffic flows over different horizons.
CODA simulates future data to generalize models across different datasets.
problem Concept drift in real-world machine learning models.
method CODA framework using a predicted feature correlation matrix to simulate future data.
result CODA effectively achieves temporal domain generalization across different model architectures.
The spatio-temporal graph learning is becoming an increasingly important object of graph study. Many application domains involve highly dynamic graphs where temporal information is crucial, e.g. traffic networks and financial transaction graphs. Despite the constant progress made on learning structured data, there is s…
Acoustic scenes are rich and redundant in their content. In this work, we present a spatio-temporal attention pooling layer coupled with a convolutional recurrent neural network to learn from patterns that are discriminative while suppressing those that are irrelevant for acoustic scene classification. The convolutiona…
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.
We propose Power Slow Feature Analysis, a gradient-based method to extract temporally slow features from a high-dimensional input stream that varies on a faster time-scale, as a variant of Slow Feature Analysis (SFA) that allows end-to-end training of arbitrary differentiable architectures and thereby significantly ext…
Novel deep learning model for multivariate time series prediction.
problem Challenges in multivariate time series prediction with correlations and complex temporal patterns.
method Temporal Tensor Transformation Network (TTNT) that transforms multivariate time series into tensors for improved feature extraction.
result TTNT outperforms state-of-the-art methods in window-based predictions across various tasks.
We propose the application of a high-speed maximum likelihood clustering algorithm to detect temporal financial market states, using correlation matrices estimated from intraday market microstructure features. We first determine the ex-ante intraday temporal cluster configurations to identify market states, and then st…
Paper analyzes TD(λ) convergence rates for arbitrary features.
problem Convergence rates for linear TD(λ) under arbitrary features. method Developed a novel stochastic approximation result for arbitrary features.
result Established L2 convergence rates for linear TD(λ) without linearly independent features assumption. TFiLM expands convolutional models' receptive field with minimal overhead.
problem Capturing long-range dependencies in sequential data.
method A novel architectural component using a recurrent neural network to modulate convolutional model activations.
result TFiLM significantly improves learning speed and accuracy on various tasks.
MTRGL learns temporal correlations from multi-modal data for improved pair trading.
problem Discerning temporal correlations among financial entities.
method Combines time series data and discrete features into a temporal graph, using a memory-based temporal graph neural network.
result MTRGL outperforms traditional methods in temporal graph link prediction and pair trading.
Evolutionary clustering aims at capturing the temporal evolution of clusters. This issue is particularly important in the context of social media data that are naturally temporally driven. In this paper, we propose a new probabilistic model-based evolutionary clustering technique. The Temporal Multinomial Mixture (TMM)…
Proposes a dynamic model for urban traffic volume prediction.
problem Urban traffic volume prediction for better traffic management and driver planning.
method Combines bidirectional LSTM, attention mechanism, and external features.
result Improves prediction precision by 3-7 percent on NYC-Taxi and NYC-Bike datasets.
Whole MILC learns brain disorder dynamics from unlabeled data.
problem Learning spatio-temporal brain disorder dynamics from unlabeled data.
method Self-supervised pre-training of whole MILC on unlabeled healthy control data.
result Whole MILC outperforms existing methods and provides diagnostic insights.
A novel distance measure aligns time series with feature and temporal variability.
problem Measuring similarity between time series with different features and dynamics.
method Learn a latent global transformation and temporal alignment in a joint optimization problem.
result Framework robustly aligns time series across various invariance classes.
Proposes a new tensor decomposition method for functional temporal data with adaptive complexity.
problem Challenges in temporal tensor decomposition for general tensor data with continuous indexes.
method Encodes continuous spatial indexes as learnable Fourier features and uses neural ODEs for temporal trajectories. Introduces a sparsity-inducing prior for complexity adaptation.
result Significantly outperforms existing methods in prediction performance and robustness against noise.
Model infers temporal connections in dynamic graphs from node interactions.
problem Challenges in reasoning about evolving graphs, especially with human-specified edges.
method Temporal point processes and variational autoencoders with bilinear interactions.
result Model outperforms baselines and infers semantically interpretable connections.
When sensors collect spatio-temporal data in a large geographical area, the existence of missing data cannot be escaped. Missing data negatively impacts the performance of data analysis and machine learning algorithms. In this paper, we study deep autoencoders for missing data imputation in spatio-temporal problems. We…
CoI framework models clinical feature interactions, revealing temporal dependencies and enhancing transparency.
problem Capturing latent, time-varying dependencies among clinical features in time-series data.
method Chain-of-Influence (CoI) framework constructs an explicit, time-unfolded graph of feature interactions.
result Achieves state-of-the-art predictive performance (AUROC of 0.960 on CKD progression and 0.950 on ICU mortality).
We present the ConditionaL Neural Network (CLNN) and the Masked ConditionaL Neural Network (MCLNN) designed for temporal signal recognition. The CLNN takes into consideration the temporal nature of the sound signal and the MCLNN extends upon the CLNN through a binary mask to preserve the spatial locality of the feature…
STNN models forecast COVID-19 spread with improved accuracy.
problem Forecasting the spread of COVID-19 worldwide.
method Spatio-temporal Neural Network (STNN) incorporating spatial and temporal data.
result STNN models outperform classical models in accuracy and handling both spatial and temporal data.
Study analyzes seasonal hydroclimatic features across climates and continents.
problem Lack of seasonal hydroclimatic feature analysis for Koppen-Geiger climates and continents.
method Global-scale analysis of 13,000 time series using 7 features.
result Notable differences in feature magnitudes across Koppen-Geiger climate classes and continental regions.