Advancing Time Series Forecasting with Large Language Models: Innovations and Applications

Dear Colleagues,

Over the last decade, remarkable progress has been made in large language models (LLMs), demonstrating their exceptional accuracy in performing complex natural language tasks. Recent advances have shown that pre-trained LLMs can be exploited to capture complex dependencies in time series data and facilitate various applications, including forecasting. The flexibility of LLMs, stemming from the diverse models available and the various ways in which they can be configured for time series analysis, makes them highly adaptable to a wide range of domain-specific applications, particularly in fields such as economics and finance.

This Special Issue welcomes high-quality papers that introduce novel forecasting applications of LLMs in economics and finance or present new methodological advancements.

This Special Issue welcomes manuscripts that link the following themes:

• Forecasting asset returns and volatilities with LLMs;
• Tail risk forecasting with LLMs;
• Forecasting economics with LLMs;
• Forecasting business cycles with LLMs;
• Forecasting economics with LLMs;
• Detecting regimes with LLMs.

We look forward to receiving your original research articles and reviews.

Dr. Manuela Pedio
Prof. Dr. Massimo Guidolin
Dr. Walayat Hussain
Prof. Dr. Kaijian He
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Forecasting is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• large language models
• time series forecasting
• asset returns and volatilities
• economic forecasting
• business cycles and regimes
• tail risk
• non-linear time-series models
• forecasting methods
• artificial intelligence applications
• dynamic portfolio choice
• empirical option pricing
• asset pricing