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Found 38 papers, 16 papers with code
iRangeGraph: Improvising Range-dedicated Graphs for Range-filtering Nearest Neighbor Search
In this study, instead of materializing a compressed index for every possible query range in preparation for querying, we materialize graph-based indexes, called elemental graphs, for a moderate number of ranges.
UniTE: A Survey and Unified Pipeline for Pre-training ST Trajectory Embeddings
However, research progress on this topic faces two key challenges: a lack of a comprehensive overview of existing methods, resulting in several related methods not being well-recognized, and the absence of a unified pipeline, complicating the development new methods and the analysis of methods.
A Survey of Generative Techniques for Spatial-Temporal Data Mining
By integrating generative techniques and providing a standardized framework, the paper contributes to advancing the field and encourages researchers to explore the vast potential of generative techniques in spatial-temporal data mining.
A Unified Replay-based Continuous Learning Framework for Spatio-Temporal Prediction on Streaming Data
The widespread deployment of wireless and mobile devices results in a proliferation of spatio-temporal data that is used in applications, e. g., traffic prediction, human mobility mining, and air quality prediction, where spatio-temporal prediction is often essential to enable safety, predictability, or reliability.
QCore: Data-Efficient, On-Device Continual Calibration for Quantized Models -- Extended Version
The first difficulty in enabling continual calibration on the edge is that the full training data may be too large and thus not always available on edge devices.
TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods
Next, we employ TFB to perform a thorough evaluation of 21 Univariate Time Series Forecasting (UTSF) methods on 8, 068 univariate time series and 14 Multivariate Time Series Forecasting (MTSF) methods on 25 datasets.
Graph Augmentation for Recommendation
To address these challenges, we propose a principled framework called GraphAug.
E2USD: Efficient-yet-effective Unsupervised State Detection for Multivariate Time Series
We propose E2Usd that enables efficient-yet-accurate unsupervised MTS state detection.
UVTM: Universal Vehicle Trajectory Modeling with ST Feature Domain Generation
However, most methods target only one specific task and cannot be applied universally.
Disentangling Imperfect: A Wavelet-Infused Multilevel Heterogeneous Network for Human Activity Recognition in Flawed Wearable Sensor Data
To address these challenges, we propose a multilevel heterogeneous neural network, called MHNN, for sensor data analysis.
Learning Time-aware Graph Structures for Spatially Correlated Time Series Forecasting
Spatio-temporal forecasting of future values of spatially correlated time series is important across many cyber-physical systems (CPS).
Missing Value Imputation for Multi-attribute Sensor Data Streams via Message Propagation (Extended Version)
First, we propose a message propagation imputation network (MPIN) that is able to recover the missing values of data instances in a time window.
Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysis
Moreover, based on the proposed BasicTS and rich heterogeneous MTS datasets, we conduct an exhaustive and reproducible performance and efficiency comparison of popular models, providing insights for researchers in selecting and designing MTS forecasting models.
LightPath: Lightweight and Scalable Path Representation Learning
Next, we propose a relational reasoning framework to enable faster training of more robust sparse path encoders.
Origin-Destination Travel Time Oracle for Map-based Services
Given an origin (O), a destination (D), and a departure time (T), an Origin-Destination (OD) travel time oracle~(ODT-Oracle) returns an estimate of the time it takes to travel from O to D when departing at T. ODT-Oracles serve important purposes in map-based services.
CHGNN: A Semi-Supervised Contrastive Hypergraph Learning Network
However, recent studies on hypergraph learning that extend graph convolutional networks to hypergraphs cannot learn effectively from features of unlabeled data.
LightTS: Lightweight Time Series Classification with Adaptive Ensemble Distillation -- Extended Version
First, we propose adaptive ensemble distillation that assigns adaptive weights to different base models such that their varying classification capabilities contribute purposefully to the training of the lightweight model.
LightCTS: A Lightweight Framework for Correlated Time Series Forecasting
Many deep learning models have been proposed to improve the accuracy of CTS forecasting.
Ranked #1 on
Traffic Prediction
on PeMS04
(FLOPs(M) metric, using extra
training data)
Computational Efficiency
Correlated Time Series Forecasting
+4
A Pattern Discovery Approach to Multivariate Time Series Forecasting
To capture the temporal and multivariate correlations among subsequences, we design a pattern discovery model, that constructs correlations via diverse pattern functions.
Joint Neural Architecture and Hyperparameter Search for Correlated Time Series Forecasting
To overcome these limitations, we propose SEARCH, a joint, scalable framework, to automatically devise effective CTS forecasting models.
A Comparative Study on Unsupervised Anomaly Detection for Time Series: Experiments and Analysis
The continued digitization of societal processes translates into a proliferation of time series data that cover applications such as fraud detection, intrusion detection, and energy management, where anomaly detection is often essential to enable reliability and safety.
Design Automation for Fast, Lightweight, and Effective Deep Learning Models: A Survey
A key challenge is that while the application of deep models often incurs substantial memory and computational costs, edge devices typically offer only very limited storage and computational capabilities that may vary substantially across devices.
Pre-training General Trajectory Embeddings with Maximum Multi-view Entropy Coding
Spatio-temporal trajectories provide valuable information about movement and travel behavior, enabling various downstream tasks that in turn power real-world applications.
Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting
However, intuitively, traffic data encompasses two different kinds of hidden time series signals, namely the diffusion signals and inherent signals.
Ranked #4 on
Traffic Prediction
on PEMS-BAY
Robust and Explainable Autoencoders for Unsupervised Time Series Outlier Detection---Extended Version
This is an extended version of "Robust and Explainable Autoencoders for Unsupervised Time Series Outlier Detection", to appear in IEEE ICDE 2022.
Weakly-supervised Temporal Path Representation Learning with Contrastive Curriculum Learning -- Extended Version
In this setting, it is essential to learn generic temporal path representations(TPRs) that consider spatial and temporal correlations simultaneously and that can be used in different applications, i. e., downstream tasks.
AutoCTS: Automated Correlated Time Series Forecasting -- Extended Version
Specifically, we design both a micro and a macro search space to model possible architectures of ST-blocks and the connections among heterogeneous ST-blocks, and we provide a search strategy that is able to jointly explore the search spaces to identify optimal forecasting models.
Deep Spatially and Temporally Aware Similarity Computation for Road Network Constrained Trajectories
Trajectory similarity computation has drawn massive attention, as it is core functionality in a wide range of applications such as ride-sharing, traffic analysis, and social recommendation.
Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles -- Extended Version
To improve accuracy, the ensemble employs multiple basic outlier detection models built on convolutional sequence-to-sequence autoencoders that can capture temporal dependencies in time series.
UniTE -- The Best of Both Worlds: Unifying Function-Fitting and Aggregation-Based Approaches to Travel Time and Travel Speed Estimation
An empirical study finds that an instance of UniTE can improve the accuracies of travel speed distribution and travel time estimation by $40-64\%$ and $3-23\%$, respectively, compared to using function fitting or aggregation alone
SOUP: Spatial-Temporal Demand Forecasting and Competitive Supply
In this setting, an assignment authority is to assign agents to requests such that the average idle time of the agents is minimized.
Databases Signal Processing
Relational Fusion Networks: Graph Convolutional Networks for Road Networks
The application of machine learning techniques in the setting of road networks holds the potential to facilitate many important intelligent transportation applications.
On Network Embedding for Machine Learning on Road Networks: A Case Study on the Danish Road Network
This is problematic for analysis tasks that rely on such information for machine learning.
Graph Convolutional Networks for Road Networks
In addition, we provide experimental evidence of the short-comings of state-of-the-art GCNs in the context of road networks: unlike our method, they cannot effectively leverage the road network structure for road segment classification and fail to outperform a regular multi-layer perceptron.
Recurrent Multi-Graph Neural Networks for Travel Cost Prediction
Origin-destination (OD) matrices are often used in urban planning, where a city is partitioned into regions and an element (i, j) in an OD matrix records the cost (e. g., travel time, fuel consumption, or travel speed) from region i to region j.
Learning to Route with Sparse Trajectory Sets---Extended Version
In the second step, we exploit the above graph-like structure to achieve a comprehensive trajectory-based routing solution.
Using Incomplete Information for Complete Weight Annotation of Road Networks -- Extended Version
This paper formulates and addresses the problem of annotating all edges in a road network with travel cost based weights from a set of trips in the network that cover only a small fraction of the edges, each with an associated ground-truth travel cost.
