Adapter-Tuning GPT-2 for Time-Series Forecasting in Trading Applications

Summary

The article explains how to fine-tune a pre-trained GPT-2 model with adapter modules and compare this approach with LoRA and full-parameter fine-tuning. Its adapter design projects hidden features into a smaller bottleneck, applies a nonlinear activation and dropout, then projects them back. The article also describes integrating adapters into a modified GPT-2 model and outlines a forecasting example that feeds a sequence of market data points to predict a longer sequence.

The comparison is presented as a guide to choosing among methods: adapters provide modular task-specific parameters and may suit multi-task use, while LoRA is described as more parameter-efficient. The author reports that adapter tuning can take somewhat longer and use more VRAM than LoRA, while positioning full fine-tuning as a baseline. The forecast setup is acknowledged as aggressive relative to a more conservative prediction horizon. The article does not establish live-trading effectiveness; choosing input and output lengths and evaluating results across currency pairs and periods are left for further testing.

Key ideas

• Adapter-tuning adds trainable modules within a pre-trained model while retaining its main parameters.
• The described adapter uses a bottleneck projection, activation, dropout, and projection back to the original feature size.
• The article compares adapter tuning with LoRA and full-parameter fine-tuning in training and inference considerations.
• Its example uses an aggressive forecasting horizon that may not fit practical trading directly.
• Forecast settings and trading performance require further testing across instruments and periods.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.