TSCOMP-Agent: Automated Decomposition and Reassembly of Time Series Forecasting Models via LLM Agents

The time-series forecasting literature is expanding rapidly, with new methods spanning an increasingly broad range of architectural paradigms. Many advances arise from specific mechanisms ranging from series normalization and decomposition to specialized architecture design, whose effectiveness often varies across diverse forecasting tasks. This motivates the question of whether effective mechanisms from different models can be identified as reusable components and composed into stronger forecasting architectures. Recent studies investigate this modular perspective, yet remain confined to manual expert curation, rendering continuous expansion unsustainable as the forecasting literature rapidly evolves. We introduce TSCOMP-AGENT, an LLM-based multi-agent framework that automatically decomposes forecasting models into verified and reusable components for automated reassembly. Building on a taxonomy-structured knowledge base, TSCOMP-AGENT synthesizes proposals from independent exploration agents into an executable plan. The implementation agent then translates this plan into modular components through iterative review and repair. Global consolidation finally verifies their compatibility and reusability before admission into the shared component pool. Experimental results verify that TSCOMP-AGENT reliably extends the component pool from diverse forecasting models across varied LLM instantiations. Models reassembled from this pool match or outperform human-designed baselines on multiple datasets, even under historical-cutoff and backbone-constrained settings, providing a scalable mechanism to accumulate design knowledge of time-series forecasting and facilitate automated architecture discovery.