Saved in:
| Main Authors: | Lee, Gawon, Park, Hanbyeol, Kim, Minseop, Kim, Dohee, Bae, Hyerim |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.20611 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
IConv: Focusing on Local Variation with Channel Independent Convolution for Multivariate Time Series Forecasting
by: Lee, Gawon, et al.
Published: (2025)
by: Lee, Gawon, et al.
Published: (2025)
Domain-Adaptive Health Indicator Learning with Degradation-Stage Synchronized Sampling and Cross-Domain Autoencoder
by: Choo, Jungho, et al.
Published: (2026)
by: Choo, Jungho, et al.
Published: (2026)
JustDense: Just using Dense instead of Sequence Mixer for Time Series analysis
by: Park, TaekHyun, et al.
Published: (2025)
by: Park, TaekHyun, et al.
Published: (2025)
Generative AI and Machine Learning Collaboration for Container Dwell Time Prediction via Data Standardization
by: Kim, Minseop, et al.
Published: (2026)
by: Kim, Minseop, et al.
Published: (2026)
LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models
by: Park, Taekhyun, et al.
Published: (2026)
by: Park, Taekhyun, et al.
Published: (2026)
Correlation recurrent units: A novel neural architecture for improving the predictive performance of time-series data
by: Sim, Sunghyun, et al.
Published: (2022)
by: Sim, Sunghyun, et al.
Published: (2022)
Application of Large Language Models for Container Throughput Forecasting: Incorporating Contextual Information in Port Logistics
by: Kim, Minseop, et al.
Published: (2026)
by: Kim, Minseop, et al.
Published: (2026)
FEATHer: Fourier-Efficient Adaptive Temporal Hierarchy Forecaster for Time-Series Forecasting
by: Lee, Jaehoon, et al.
Published: (2026)
by: Lee, Jaehoon, et al.
Published: (2026)
Process-Aware Procurement Lead Time Prediction for Shipyard Delay Mitigation
by: Lee, Yongjae, et al.
Published: (2026)
by: Lee, Yongjae, et al.
Published: (2026)
Distributed Lag Transformer based on Time-Variable-Aware Learning for Explainable Multivariate Time Series Forecasting
by: Kim, Younghwi, et al.
Published: (2024)
by: Kim, Younghwi, et al.
Published: (2024)
LAST-RAG: Literature-Anchored Stochastic Trajectory Retrieval-Augmented Generation for Knowledge-Conditioned Degradation Model Selection
by: Park, Hanbyeol, et al.
Published: (2026)
by: Park, Hanbyeol, et al.
Published: (2026)
Are Self-Attentions Effective for Time Series Forecasting?
by: Kim, Dongbin, et al.
Published: (2024)
by: Kim, Dongbin, et al.
Published: (2024)
A Surrogate Model for Quay Crane Scheduling Problem
by: Park, Kikun, et al.
Published: (2024)
by: Park, Kikun, et al.
Published: (2024)
Artificial Intelligence-based Smart Port Logistics Metaverse for Enhancing Productivity, Environment, and Safety in Port Logistics: A Case Study of Busan Port
by: Sim, Sunghyun, et al.
Published: (2024)
by: Sim, Sunghyun, et al.
Published: (2024)
Diffusion Bridge AutoEncoders for Unsupervised Representation Learning
by: Kim, Yeongmin, et al.
Published: (2024)
by: Kim, Yeongmin, et al.
Published: (2024)
A Lightweight CNN-Transformer Model for Learning Traveling Salesman Problems
by: Jung, Minseop, et al.
Published: (2023)
by: Jung, Minseop, et al.
Published: (2023)
TimePerceiver: An Encoder-Decoder Framework for Generalized Time-Series Forecasting
by: Lee, Jaebin, et al.
Published: (2025)
by: Lee, Jaebin, et al.
Published: (2025)
FLAG: Flow Policy MaxEnt-RL by Latent Augmented Guidance
by: Kim, Sungha, et al.
Published: (2026)
by: Kim, Sungha, et al.
Published: (2026)
Leveraging Temporally Extended Behavior Sharing for Multi-task Reinforcement Learning
by: Lee, Gawon, et al.
Published: (2025)
by: Lee, Gawon, et al.
Published: (2025)
Channel-wise Retrieval for Multivariate Time Series Forecasting
by: Kang, Junhyeok, et al.
Published: (2026)
by: Kang, Junhyeok, et al.
Published: (2026)
Long‐term forecasting of maritime economics index using time‐series decomposition and two‐stage attention
by: Dohee Kim, et al.
Published: (2024)
by: Dohee Kim, et al.
Published: (2024)
A Scalable and Transferable Time Series Prediction Framework for Demand Forecasting
by: Park, Young-Jin, et al.
Published: (2024)
by: Park, Young-Jin, et al.
Published: (2024)
Adaptive Information Routing for Multimodal Time Series Forecasting
by: Seo, Jun, et al.
Published: (2025)
by: Seo, Jun, et al.
Published: (2025)
When Will It Fail?: Anomaly to Prompt for Forecasting Future Anomalies in Time Series
by: Park, Min-Yeong, et al.
Published: (2025)
by: Park, Min-Yeong, et al.
Published: (2025)
ContrastCAD: Contrastive Learning-based Representation Learning for Computer-Aided Design Models
by: Jung, Minseop, et al.
Published: (2024)
by: Jung, Minseop, et al.
Published: (2024)
NanoQuant: Efficient Sub-1-Bit Quantization of Large Language Models
by: Chong, Hyochan, et al.
Published: (2026)
by: Chong, Hyochan, et al.
Published: (2026)
T1: One-to-One Channel-Head Binding for Multivariate Time-Series Imputation
by: Park, Dongik, et al.
Published: (2026)
by: Park, Dongik, et al.
Published: (2026)
Continuous-Time Linear Positional Embedding for Irregular Time Series Forecasting
by: Kim, Byunghyun, et al.
Published: (2024)
by: Kim, Byunghyun, et al.
Published: (2024)
Counterfactual Explanation for Auto-Encoder Based Time-Series Anomaly Detection
by: Srinivasan, Abhishek, et al.
Published: (2025)
by: Srinivasan, Abhishek, et al.
Published: (2025)
Towards Foundation Auto-Encoders for Time-Series Anomaly Detection
by: González, Gastón García, et al.
Published: (2025)
by: González, Gastón García, et al.
Published: (2025)
Battling the Non-stationarity in Time Series Forecasting via Test-time Adaptation
by: Kim, HyunGi, et al.
Published: (2025)
by: Kim, HyunGi, et al.
Published: (2025)
$K^2$VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting
by: Wu, Xingjian, et al.
Published: (2025)
by: Wu, Xingjian, et al.
Published: (2025)
Harmonic Dataset Distillation for Time Series Forecasting
by: Hong, Seungha, et al.
Published: (2026)
by: Hong, Seungha, et al.
Published: (2026)
Transformers with Attentive Federated Aggregation for Time Series Stock Forecasting
by: Thwal, Chu Myaet, et al.
Published: (2024)
by: Thwal, Chu Myaet, et al.
Published: (2024)
Addressing Prediction Delays in Time Series Forecasting: A Continuous GRU Approach with Derivative Regularization
by: Jhin, Sheo Yon, et al.
Published: (2024)
by: Jhin, Sheo Yon, et al.
Published: (2024)
A Comprehensive Survey of Deep Learning for Time Series Forecasting: Architectural Diversity and Open Challenges
by: Kim, Jongseon, et al.
Published: (2024)
by: Kim, Jongseon, et al.
Published: (2024)
Sequential Order-Robust Mamba for Time Series Forecasting
by: Lee, Seunghan, et al.
Published: (2024)
by: Lee, Seunghan, et al.
Published: (2024)
Time Series Forecasting Using a Hybrid Deep Learning Method: A Bi-LSTM Embedding Denoising Auto Encoder Transformer
by: Koohfar, Sahar, et al.
Published: (2025)
by: Koohfar, Sahar, et al.
Published: (2025)
TiVaT: A Transformer with a Single Unified Mechanism for Capturing Asynchronous Dependencies in Multivariate Time Series Forecasting
by: Ha, Junwoo, et al.
Published: (2024)
by: Ha, Junwoo, et al.
Published: (2024)
AMLNet: Adversarial Mutual Learning Neural Network for Non-AutoRegressive Multi-Horizon Time Series Forecasting
by: Lin, Yang
Published: (2023)
by: Lin, Yang
Published: (2023)
Similar Items
-
IConv: Focusing on Local Variation with Channel Independent Convolution for Multivariate Time Series Forecasting
by: Lee, Gawon, et al.
Published: (2025) -
Domain-Adaptive Health Indicator Learning with Degradation-Stage Synchronized Sampling and Cross-Domain Autoencoder
by: Choo, Jungho, et al.
Published: (2026) -
JustDense: Just using Dense instead of Sequence Mixer for Time Series analysis
by: Park, TaekHyun, et al.
Published: (2025) -
Generative AI and Machine Learning Collaboration for Container Dwell Time Prediction via Data Standardization
by: Kim, Minseop, et al.
Published: (2026) -
LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models
by: Park, Taekhyun, et al.
Published: (2026)