From Tokenizer Bias to Backbone Capability: A Controlled Study of LLMs for Time Series Forecasting

ArXi:2504.08818v2 Announce Type: replace-cross Using pre-trained large language models (LLMs) as a backbone for time series prediction has recently attracted growing research interest. Existing approaches typically split time series into patches, map them to the token space of LLMs via a Tokenizer, process the tokens through a frozen or fine-tuned LLM backbone, and then reconstruct numerical forecasts using a Detokenizer. However, the actual effectiveness of LLMs for time series forecasting remains under debate.