Arık, Sercan Ö.

Temporal Fusion Transformers for interpretable multi-horizon time series forecasting

Lim, B., Arık, S. Ö., Loeff, N., & Pfister, T. (2021) · International Journal of Forecasting

Time Series Analysis and ForecastingForecasting Techniques and Applications

Cited by 3,291

Learn-by-interact: A Data-Centric Framework for Self-Adaptive Agents in Realistic Environments

Su, H., Sun, R., Yoon, J., Yin, P., Yu, T., & Arık, S. Ö. (2025) · arXiv

Topic ModelingSoftware Engineering Research

Cited by 2

MLE-STAR: Machine Learning Engineering Agent via Search and Targeted Refinement

Nam, J., Yoon, J., Chen, J., Shin, J., Arık, S. Ö., & Pfister, T. (2025) · arXiv

Software Engineering ResearchMachine Learning and Data Classification

Cited by 1

Multi-Agent Design: Optimizing Agents with Better Prompts and Topologies

Zhou, H., Wan, X., Sun, R., Palangi, H., Iqbal, S., Vulić, I., Korhonen, A., & Arık, S. Ö. (2025) · arXiv

Multi-Agent Systems and NegotiationNatural Language Processing Techniques

Cited by 0