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Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting

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arxiv 2306.11025 v1 pith:T43CKFCY submitted 2023-06-19 cs.LG cs.AIcs.CLq-fin.ST

Xinli Yu , Zheng Chen , Yuan Ling , Shujing Dong , Zongyi Liu , Yanbin Lu This is my paper
classification cs.LGcs.AIcs.CLq-fin.ST
keywords financialmodelseriestimeexplainablehistoricalknowledgellms
verification ladder T0 review T1 audit T2 compute T3 formal
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This paper presents a novel study on harnessing Large Language Models' (LLMs) outstanding knowledge and reasoning abilities for explainable financial time series forecasting. The application of machine learning models to financial time series comes with several challenges, including the difficulty in cross-sequence reasoning and inference, the hurdle of incorporating multi-modal signals from historical news, financial knowledge graphs, etc., and the issue of interpreting and explaining the model results. In this paper, we focus on NASDAQ-100 stocks, making use of publicly accessible historical stock price data, company metadata, and historical economic/financial news. We conduct experiments to illustrate the potential of LLMs in offering a unified solution to the aforementioned challenges. Our experiments include trying zero-shot/few-shot inference with GPT-4 and instruction-based fine-tuning with a public LLM model Open LLaMA. We demonstrate our approach outperforms a few baselines, including the widely applied classic ARMA-GARCH model and a gradient-boosting tree model. Through the performance comparison results and a few examples, we find LLMs can make a well-thought decision by reasoning over information from both textual news and price time series and extracting insights, leveraging cross-sequence information, and utilizing the inherent knowledge embedded within the LLM. Additionally, we show that a publicly available LLM such as Open-LLaMA, after fine-tuning, can comprehend the instruction to generate explainable forecasts and achieve reasonable performance, albeit relatively inferior in comparison to GPT-4.

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Cited by 10 Pith papers

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    SMT-AD detects anomalies via superposed multiresolution bond-dimension-1 MPOs with Fourier embedding, claiming competitive baseline performance and linear parameter scaling.

  5. A Survey of Reasoning and Agentic Systems in Time Series with Large Language Models

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  9. On Identifying Why and When Foundation Models Perform Well on Time-Series Forecasting Using Automated Explanations and Rating

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