StockTime: A Time Series Specialized Large Language Model Architecture for Stock Price Prediction
The stock price prediction task holds a significant role in the financial domain and has been studied for a long time. Recently, large language models (LLMs) have brought new ways to improve these predictions. While recent financial large language models (FinLLMs) have shown considerable progress in...
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Main Authors | , , , , , , |
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Format | Journal Article |
Language | English |
Published |
24.08.2024
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Subjects | |
Online Access | Get full text |
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Summary: | The stock price prediction task holds a significant role in the financial
domain and has been studied for a long time. Recently, large language models
(LLMs) have brought new ways to improve these predictions. While recent
financial large language models (FinLLMs) have shown considerable progress in
financial NLP tasks compared to smaller pre-trained language models (PLMs),
challenges persist in stock price forecasting. Firstly, effectively integrating
the modalities of time series data and natural language to fully leverage these
capabilities remains complex. Secondly, FinLLMs focus more on analysis and
interpretability, which can overlook the essential features of time series
data. Moreover, due to the abundance of false and redundant information in
financial markets, models often produce less accurate predictions when faced
with such input data. In this paper, we introduce StockTime, a novel LLM-based
architecture designed specifically for stock price data. Unlike recent FinLLMs,
StockTime is specifically designed for stock price time series data. It
leverages the natural ability of LLMs to predict the next token by treating
stock prices as consecutive tokens, extracting textual information such as
stock correlations, statistical trends and timestamps directly from these stock
prices. StockTime then integrates both textual and time series data into the
embedding space. By fusing this multimodal data, StockTime effectively predicts
stock prices across arbitrary look-back periods. Our experiments demonstrate
that StockTime outperforms recent LLMs, as it gives more accurate predictions
while reducing memory usage and runtime costs. |
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DOI: | 10.48550/arxiv.2409.08281 |