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Found 14 papers, 3 papers with code
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.
UVTM: Universal Vehicle Trajectory Modeling with ST Feature Domain Generation
However, most methods target only one specific task and cannot be applied universally.
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).
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.
A Crystal-Specific Pre-Training Framework for Crystal Material Property Prediction
To overcome these challenges, we propose the crystal-specific pre-training framework for learning crystal representations with self-supervision.
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.
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.
BaLeNAS: Differentiable Architecture Search via the Bayesian Learning Rule
Differentiable Architecture Search (DARTS) has received massive attention in recent years, mainly because it significantly reduces the computational cost through weight sharing and continuous relaxation.
Unsupervised Path Representation Learning with Curriculum Negative Sampling
In the global view, PIM distinguishes the representations of the input paths from those of the negative paths.
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
Infinitely Wide Graph Convolutional Networks: Semi-supervised Learning via Gaussian Processes
Graph convolutional neural networks~(GCNs) have recently demonstrated promising results on graph-based semi-supervised classification, but little work has been done to explore their theoretical properties.
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.
