{"slug": "efficient-test-time-scaling-for-llm-based-time-series-forecasting", "title": "Efficient Test-Time Scaling for LLM-based Time Series Forecasting", "summary": "Researchers propose SCALER, a coarse-to-fine forecasting framework that uses a lightweight Transformer to predict the global shape of future time series, guiding an LLM to perform test-time scaling via iterative residual token refinement. SCALER outperforms strong baselines in long-term, short-term, and zero-shot forecasting while significantly reducing inference cost. The code is available at https://github.com/xuanmay2701/SCALER.", "body_md": "arXiv:2608.08675v1 Announce Type: new\nAbstract: Long-term time series forecasting benefits from preserving global structure such as trends and seasonality. Recent LLM-based forecasters often improve accuracy through test-time scaling (e.g., iterative refinement), but these methods are computationally expensive and increasingly prone to global-shape mismatch as the prediction horizon extends. We propose SCALER, a coarse-to-fine forecasting framework that first employs a lightweight Transformer tailored to long-term shape modeling to predict a coarse representation of future dynamics. This predicted shape then serves as a compact guide for an LLM to perform test-time scaling via iterative coarse-to-fine residual token refinement, while processing substantially fewer tokens at each step. By guiding refinement with an explicit future-shape prediction, SCALER reduces reliance on long description prompts, and its fixed-step refinement avoids costly reward-model-based selection, further lowering computational overhead. Experimental results demonstrate that SCALER outperforms strong forecasting baselines in long-term, short-term and zero-shot forecasting while significantly reducing the inference cost associated with scaled LLM for time series forecasting. Code: https://github.com/xuanmay2701/SCALER.", "url": "https://wpnews.pro/news/efficient-test-time-scaling-for-llm-based-time-series-forecasting", "canonical_source": "https://www.machinebrief.com/news/efficient-test-time-scaling-for-llm-based-time-series-foreca-yk1f", "published_at": "2026-08-11 04:00:00+00:00", "updated_at": "2026-08-11 06:11:51.158462+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "large-language-models", "ai-research"], "entities": ["SCALER", "arXiv"], "alternates": {"html": "https://wpnews.pro/news/efficient-test-time-scaling-for-llm-based-time-series-forecasting", "markdown": "https://wpnews.pro/news/efficient-test-time-scaling-for-llm-based-time-series-forecasting.md", "text": "https://wpnews.pro/news/efficient-test-time-scaling-for-llm-based-time-series-forecasting.txt", "jsonld": "https://wpnews.pro/news/efficient-test-time-scaling-for-llm-based-time-series-forecasting.jsonld"}}