Temporal database ≈ Temporal database
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Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting Open
Forecasting the traffic flows is a critical issue for researchers and practitioners in the field of transportation. However, it is very challenging since the traffic flows usually show high nonlinearities and complex patterns. Most existin…
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Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting Open
Spatial-temporal network data forecasting is of great importance in a huge amount of applications for traffic management and urban planning. However, the underlying complex spatial-temporal correlations and heterogeneities make this proble…
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Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts Open
Spatial and temporal contextual information plays a key role for analyzing user behaviors, and is helpful for predicting where he or she will go next. With the growing ability of collecting information, more and more temporal and spatial c…
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Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting Open
Spatial-temporal data forecasting of traffic flow is a challenging task because of complicated spatial dependencies and dynamical trends of temporal pattern between different roads. Existing frameworks usually utilize given spatial adjacen…
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Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction Open
Traffic prediction has drawn increasing attention in AI research field due to the increasing availability of large-scale traffic data and its importance in the real world. For example, an accurate taxi demand prediction can assist taxi com…
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Traffic Flow Prediction via Spatial Temporal Graph Neural Network Open
Traffic flow analysis, prediction and management are keystones for building smart cities in the new era. With the help of deep neural networks and big traffic data, we can better understand the latent patterns hidden in the complex transpo…
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Spatial–Temporal Recurrent Neural Network for Emotion Recognition Open
In this paper, we propose a novel deep learning framework, called spatial-temporal recurrent neural network (STRNN), to integrate the feature learning from both spatial and temporal information of signal sources into a unified spatial-temp…
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Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series Open
Latest remote sensing sensors are capable of acquiring high spatial and spectral Satellite Image Time Series (SITS) of the world. These image series are a key component of classification systems that aim at obtaining up-to-date and accurat…
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Learning Sequence Encoders for Temporal Knowledge Graph Completion Open
Research on link prediction in knowledge graphs has mainly focused on static multi-relational data. In this work we consider temporal knowledge graphs where relations between entities may only hold for a time interval or a specific point i…
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Time-Series Representation Learning via Temporal and Contextual Contrasting Open
Learning decent representations from unlabeled time-series data with temporal dynamics is a very challenging task. In this paper, we propose an unsupervised Time-Series representation learning framework via Temporal and Contextual Contrast…
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Spatio-Temporal Data Mining Open
Large volumes of spatio-temporal data are increasingly collected and studied in diverse domains, including climate science, social sciences, neuroscience, epidemiology, transportation, mobile health, and Earth sciences. Spatio-temporal dat…
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TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis Open
Time series analysis is of immense importance in extensive applications, such as weather forecasting, anomaly detection, and action recognition. This paper focuses on temporal variation modeling, which is the common key problem of extensiv…
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Learning from History: Modeling Temporal Knowledge Graphs with Sequential Copy-Generation Networks Open
Large knowledge graphs often grow to store temporal facts that model the dynamic relations or interactions of entities along the timeline. Since such temporal knowledge graphs often suffer from incompleteness, it is important to develop ti…
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A Time-Weighted Dynamic Time Warping Method for Land-Use and Land-Cover Mapping Open
This paper presents a time-weighted version of the dynamic time warping (DTW) method for land-use and land-cover classification using remote sensing image time series. Methods based on DTW have achieved significant results in time-series d…
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Spatio-Temporal Meta-Graph Learning for Traffic Forecasting Open
Traffic forecasting as a canonical task of multivariate time series forecasting has been a significant research topic in AI community. To address the spatio-temporal heterogeneity and non-stationarity implied in the traffic stream, in this…
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Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting Open
Multivariate Time Series (MTS) forecasting plays a vital role in a wide range\nof applications. Recently, Spatial-Temporal Graph Neural Networks (STGNNs) have\nbecome increasingly popular MTS forecasting methods. STGNNs jointly model the\n…
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Spatio-Temporal Self-Supervised Learning for Traffic Flow Prediction Open
Robust prediction of citywide traffic flows at different time periods plays a crucial role in intelligent transportation systems. While previous work has made great efforts to model spatio-temporal correlations, existing methods still suff…
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Spatial-Temporal Person Re-Identification Open
Most of current person re-identification (ReID) methods neglect a spatial-temporal constraint. Given a query image, conventional methods compute the feature distances between the query image and all the gallery images and return a similari…
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Time Curves: Folding Time to Visualize Patterns of Temporal Evolution in Data Open
We introduce time curves as a general approach for visualizing patterns of evolution in temporal data. Examples of such patterns include slow and regular progressions, large sudden changes, and reversals to previous states. These patterns …
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Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networks Open
In this work, we examine a novel forecasting approach for COVID-19 case prediction that uses Graph Neural Networks and mobility data. In contrast to existing time series forecasting models, the proposed approach learns from a single large-…
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Low-Rank Autoregressive Tensor Completion for Spatiotemporal Traffic Data Imputation Open
Spatiotemporal traffic time series (e.g., traffic volume/speed) collected from sensing systems are often incomplete with considerable corruption and large amounts of missing values, preventing users from harnessing the full power of the da…
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Getting Ahead of Time—Performing Temporal Boundaries to Coordinate Routines under Temporal Uncertainty Open
In this ethnographic study of firefighters we explore how routines are coordinated under high levels of temporal uncertainty—when the timing of critical events cannot be known in advance and temporal misalignment creates substantial risks.…
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A Novel Data-Driven Model for Real-Time Influenza Forecasting Open
We propose a novel data-driven machine learning method using long short-term memory (LSTM)-based multi-stage forecasting for influenza forecasting. The novel aspects of the method include the following: 1) the introduction of LSTM method t…
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Visualizing Time-Dependent Data Using Dynamic t-SNE Open
Many interesting processes can be represented as time-dependent datasets. We define a time-dependent dataset as a sequence of datasets captured at particular time steps. In such a sequence, each dataset is composed of observations (high-di…
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TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion Open
Inferring missing facts in temporal knowledge graphs (TKGs) is a fundamental and challenging task. Previous works have approached this problem by augmenting methods for static knowledge graphs to leverage time-dependent representations. Ho…
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TLogic: Temporal Logical Rules for Explainable Link Forecasting on Temporal Knowledge Graphs Open
Conventional static knowledge graphs model entities in relational data as nodes, connected by edges of specific relation types. However, information and knowledge evolve continuously, and temporal dynamics emerge, which are expected to inf…
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An Epidemiological Neural Network Exploiting Dynamic Graph Structured Data Applied to the COVID-19 Outbreak Open
With the recent COVID-19 outbreak, we have assisted to the development of new epidemic models or the application of existing methodologies to predict the virus spread and to analyze how the different lock-down strategies can effectively in…
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Generative Adversarial Networks for Spatio-temporal Data: A Survey Open
Generative Adversarial Networks (GANs) have shown remarkable success in producing realistic-looking images in the computer vision area. Recently, GAN-based techniques are shown to be promising for spatio-temporal-based applications such as…
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Deep Temporal Clustering : Fully Unsupervised Learning of Time-Domain Features Open
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 …
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Learning from heterogeneous temporal data in electronic health records Open
Electronic health records contain large amounts of longitudinal data that are valuable for biomedical informatics research. The application of machine learning is a promising alternative to manual analysis of such data. However, the comple…