  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.
