LLM-Based Time Series Forecasting

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A Fast and Effective Solution to the Problem of Look-ahead Bias in LLMs
Dec 7, 2025Humzah Merchant, Bradford LevyLLM-Based Time Series Forecasting LLM Unlearning
University of Chicago Chicago, IL 60637
Auditable Context-Aware HFMD Forecasting with Structured LLM Agents
Nov 28, 2025Joongwon Chae, Runming Wang, Chen Xiong +5Epidemic Forecasting Explainable Time Series Forecasting
Institute of Biomedicine and Health Engineering, Tsinghua Shenzhen International Graduate School (SIGS), Tsinghua University, Shenzhen, China · The First Hospital of Hebei Medical University, Shijiazhuang, China · The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China +1

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A Fast and Effective Solution to the Problem of Look-ahead Bias in LLMs

Humzah Merchant, Bradford Levy

University of Chicago Chicago, IL 60637

LLM-Based Time Series Forecasting LLM Unlearning

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Applying LLMs to predictive tasks in finance is challenging due to look-ahead bias resulting from their training on long time-series data. This precludes the backtests typically employed in finance since retraining frontier models from scratch with a specific knowledge cutoff is prohibitive. In this paper, we introduce a fast, effective, and low-cost alternative. Our method guides generation at inference time by adjusting the logits of a large base model using a pair of smaller, specialized models -- one fine-tuned on information to be forgotten and another on information to be retained. We demonstrate that our method effectively removes both verbatim and semantic knowledge, corrects biases, and outperforms prior methods.

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Latest papers 62

A Fast and Effective Solution to the Problem of Look-ahead Bias in LLMs

Auditable Context-Aware HFMD Forecasting with Structured LLM Agents