A Critical Study on LSTM and Transformer Models for Financial Analysis and Forecasting

Surindernath Sivakumar (), Dhairya Katkoriya (), Malhar Shah (), Tanmayi Maddali () and N Prabakaran
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Surindernath Sivakumar: Vellore Institute of Technology
Dhairya Katkoriya: Vellore Institute of Technology
Malhar Shah: Vellore Institute of Technology
Tanmayi Maddali: Vellore Institute of Technology
N Prabakaran: Vellore Institute of Technology

A chapter in Machine Learning Technologies on Energy Economics and Finance, 2025, pp 203-218 from Springer

Abstract: Abstract In recent years, deep learning models have gained significant traction for time series forecasting due to their ability to capture complex temporal dependencies. Among these models, Long Short-Term Memory (LSTM) networks and Transformer deep learning models have emerged as leading approaches. This chapter provides a comprehensive review of LSTM and Transformer models in the context of time series forecasting. It explores their theoretical underpinnings, highlights advancements, and discusses their applications across various domains. Through an analysis of existing literature, it compares the strengths and limitations of each model, providing insights into their performance in different forecasting scenarios. This chapter aims to guide researchers and practitioners in selecting the appropriate model for specific time series financial analysis and forecasting tasks, considering the unique characteristics of their datasets and the desired forecasting horizon.

Keywords: Long Short-Term Memory (LSTM); Time series; Transformer model; Forecasting; Deep learning (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-3-031-95099-5_9

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DOI: 10.1007/978-3-031-95099-5_9

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