5 papers
Trie-Constrained Token Prediction with Hierarchy-Aware Semantic Alignment for HS Code Prediction
Minseop Kim, Taekhyun Park, Kikun Park +1
Harmonized System (HS) code prediction (HSP) from commodity text is essential to international trade, and its importance continues to grow in port logistics. For the purposes of su…
Generative AI and Machine Learning Collaboration for Container Dwell Time Prediction via Data Standardization
Minseop Kim, Takhyeong Kim, Taekhyun Park +2
Import container dwell time (ICDT) prediction is a key task for improving productivity in container terminals, as accurate predictions enable the reduction of container re-handling…
Application of Large Language Models for Container Throughput Forecasting: Incorporating Contextual Information in Port Logistics
Minseop Kim, Jaeeun Kwon, Hanbyeol Park +3
Recent advancements in generative artificial intelligence (AI) have demonstrated its substantial potential in various fields. However, its application in port logistics remains und…
ACFormer: Mitigating Non-linearity with Auto Convolutional Encoder for Time Series Forecasting
Gawon Lee, Hanbyeol Park, Minseop Kim +2
Time series forecasting (TSF) faces challenges in modeling complex intra-channel temporal dependencies and inter-channel correlations. Although recent research has highlighted the…
IConv: Focusing on Local Variation with Channel Independent Convolution for Multivariate Time Series Forecasting
Gawon Lee, Hanbyeol Park, Minseop Kim +2
Real-world time-series data often exhibit non-stationarity, including changing trends, irregular seasonality, and residuals. In terms of changing trends, recently proposed multi-la…