Synthetic Time Series Generation for Decision Intelligence Using Large Language Models

Alexandru Grigoraș and Florin Leon ()
Additional contact information
Alexandru Grigoraș: Faculty of Automatic Control and Computer Engineering, “Gheorghe Asachi” Technical University of Iasi, Bd. Mangeron 27, 700050 Iasi, Romania
Florin Leon: Faculty of Automatic Control and Computer Engineering, “Gheorghe Asachi” Technical University of Iasi, Bd. Mangeron 27, 700050 Iasi, Romania

Mathematics, 2024, vol. 12, issue 16, 1-17

Abstract: A model for generating synthetic time series data using pre-trained large language models is proposed. Starting with the Google T5-base model, which employs an encoder–decoder transformer architecture, the model underwent pre-training on diverse datasets. It was then fine-tuned using the QLoRA technique, which reduces computational complexity by quantizing weight parameters. The process involves the tokenization of time series data through mean scaling and quantization. The performance of the model was evaluated with fidelity, utility, and privacy metrics, showing improvements in fidelity and utility but a trade-off with reduced privacy. The proposed model offers a foundation for decision intelligence systems.

Keywords: transformer architecture; large language models; synthetic data; time series; decision intelligence (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/16/2494/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/16/2494/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:12:y:2024:i:16:p:2494-:d:1455002

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().