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20212026
most citedTime Series Forecasting (TSF) Using Various Deep Learning Models

36 citations · 49 across the 20 of their papers we have counts for

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5 papers · 1 filter

q-bio.BM2025

Evaluating and Scoring Ebolavirus Protein-protein Docking Models Using PIsToN

Azam Shirali, Vitalii Stebliankin, Jimeng Shi +2

Protein-protein docking is crucial for understanding how proteins interact. Numerous docking tools have been developed to discover possible conformations of two interacting protein…

cs.LG2025

Retrieval-Augmented Foundation Models for Water Level Prediction in the Everglades

Rahuul Rangaraj, Jimeng Shi, Rajendra Paudel +2

Accurate water level forecasting in the Everglades is essential for flood mitigation, drought management, water resource planning, and biodiversity conservation. While recent time-…

cs.LG2025

Hypercube-Based Retrieval-Augmented Generation for Scientific Question-Answering

Jimeng Shi, Sizhe Zhou, Bowen Jin +5

Large language models (LLMs) often need to incorporate external knowledge to solve theme-specific problems. Retrieval-augmented generation (RAG) has shown its high promise, empower…

cs.LG2025

How Effective are Large Time Series Models in Hydrology? A Study on Water Level Forecasting in Everglades

Rahuul Rangaraj, Jimeng Shi, Azam Shirali +3

The Everglades play a crucial role in flood and drought regulation, water resource planning, and ecosystem management in the surrounding regions. However, traditional physics-based…

cs.LG2025★ 4 cited

Deep Learning and Foundation Models for Weather Prediction: A Survey

Jimeng Shi, Azam Shirali, Bowen Jin +10

Physics-based numerical models have been the bedrock of atmospheric sciences for decades, offering robust solutions but often at the cost of significant computational resources. De…