Artificial Intelligence

    

Towards a Hybrid LSTM-Transformer Model for Financial Data: A Theoretical Approach

Authors: Basit Ali

This paper proposes a hybrid LSTM-Transformer architecture to train a Named Entity Recognition (NER) model on financial data, such as receipts and invoices. These data types are unstructured and come in various formats, making them difficult to process. The proposed model combines the sequential pattern recognition capabilities of LSTM networks with the contextual sensitivity of Transformer self-attention layers, making it well-suited for financial data applications. This study establishes a modular, design-oriented framework, complete with pseudocode and architectural explanations, to serve as a foundation for future empirical testing. This conceptual work aims to set a benchmark in financial data modeling by addressing domain-specific challenges and providing a scalable structure for subsequent validation.

Comments: 10 pages, written in English, submitted under CC BY-NC 4.0 license.

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[v1] 2025-01-28 20:19:43

Unique-IP document downloads: 377 times

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