StockTime represents a paradigm shift in applying Large Language Models (LLMs) to financial time series prediction. Unlike existing Financial LLMs (FinLLMs) that focus primarily on textual analysis and interpretation, StockTime is specifically designed for stock price time series data. The framework leverages the natural ability of LLMs to predict the next token by treating stock prices as consecutive tokens, while extracting textual information such as stock correlations, statistical trends, and timestamps directly from the stock price data itself. …
The paper "A Decoder-Only Foundation Model for Time-Series Forecasting" introduces a groundbreaking approach in the field of time-series forecasting, leveraging the power of decoder-only models, commonly used in natural language processing, to achieve remarkable zero-shot forecasting capabilities across a variety of domains. …
In the realm of machine learning, the Transformer model has been nothing short of revolutionary. Originating from the field of natural language processing, its ability to capture sequential relationships in data has set new benchmarks across various applications. However, its adaptation to the specific nuances of time series data has remained a complex challenge, until now. …