Text2TimeSeries: Enhancing Financial Forecasting through Time Series Prediction Updates with Event-Driven Insights from Large Language Models

TL;DR

This paper introduces Text2TimeSeries, a novel framework that integrates event-driven insights from large language models to improve financial time series forecasting by incorporating textual event data.

Contribution

The paper presents a new collaborative modeling approach that combines numerical time series data with textual event information using large language models for enhanced financial predictions.

Findings

Improved forecasting accuracy on financial market data.

Effective integration of textual event insights into time series models.

Demonstrated benefits over traditional numerical-only models.

Abstract

Time series models, typically trained on numerical data, are designed to forecast future values. These models often rely on weighted averaging techniques over time intervals. However, real-world time series data is seldom isolated and is frequently influenced by non-numeric factors. For instance, stock price fluctuations are impacted by daily random events in the broader world, with each event exerting a unique influence on price signals. Previously, forecasts in financial markets have been approached in two main ways: either as time-series problems over price sequence or sentiment analysis tasks. The sentiment analysis tasks aim to determine whether news events will have a positive or negative impact on stock prices, often categorizing them into discrete labels. Recognizing the need for a more comprehensive approach to accurately model time series prediction, we propose a collaborative modeling framework that incorporates textual information about relevant events for predictions. Specifically, we leverage the intuition of large language models about future changes to update real number time series predictions. We evaluated the effectiveness of our approach on financial market data.