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Found 6 papers, 3 papers with code
TESTAM: A Time-Enhanced Spatio-Temporal Attention Model with Mixture of Experts
In this paper, we propose a novel deep learning model named TESTAM, which individually models recurring and non-recurring traffic patterns by a mixture-of-experts model with three experts on temporal modeling, spatio-temporal modeling with static graph, and dynamic spatio-temporal dependency modeling with dynamic graph.
CLAMS: A Cluster Ambiguity Measure for Estimating Perceptual Variability in Visual Clustering
In this research, we study perceptual variability in conducting visual clustering, which we call Cluster Ambiguity.
TILDE-Q: A Transformation Invariant Loss Function for Time-Series Forecasting
In this paper, we examine the definition of shape and distortions, which are crucial for shape-awareness in time-series forecasting, and provide a design rationale for the shape-aware loss function.
A Visual Analytics System for Improving Attention-based Traffic Forecasting Models
With deep learning (DL) outperforming conventional methods for different tasks, much effort has been devoted to utilizing DL in various domains.
Learning to Remember Patterns: Pattern Matching Memory Networks for Traffic Forecasting
To evaluate the validness of the new perspective, we design a novel traffic forecasting model, called Pattern-Matching Memory Networks (PM-MemNet), which learns to match input data to the representative patterns with a key-value memory structure.
An Empirical Experiment on Deep Learning Models for Predicting Traffic Data
For example, it is difficult to figure out which models provide state-of-the-art performance, as recently proposed models have often been evaluated with different datasets and experiment environments.
