Discussing the article: "Neural Networks in Trading: Using Language Models for Time Series Forecasting"

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We continue to study time series forecasting models. In this article, we get acquainted with a complex algorithm built on the use of a pre-trained language model.

The authors of the paper "TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting" address the critical challenge of adapting large pre-trained models for time series forecasting. They propose TEMPO, a comprehensive model based on GPT, designed for effective time series representation learning. TEMPO consists of two key analytical components: one focusing on modeling specific time series patterns such as trends and seasonality, and the other aimed at deriving more generalized insights from intrinsic data properties through a prompt-based approach. Specifically, TEMPO first decomposes the original multimodal time series data into three components: trend, seasonality, and residuals. Each component is then mapped into a corresponding latent space to construct the initial time series embedding for GPT.

The authors conduct a formal analysis linking the time series domain with the frequency domain to emphasize the necessity of decomposing such components for time series analysis. They also theoretically demonstrate that the attention mechanism struggles to perform this decomposition automatically.

TEMPO uses prompts that encode temporal knowledge about trends and seasonality, effectively fine-tuning GPT for forecasting tasks. Additionally, trend, seasonality, and residuals are used to provide an interpretable structure for understanding the interactions between the original components.

Author: Dmitriy Gizlyk