Time Series Forecasting with LSTM: an extensive content analysis

Pranolo, Andri
Zhou, Xiaofeng
Mao, Yingchi

Knowledge Engineering and Data Science

This paper presents a comprehensive bibliometric and content review of the trend, architecture, and application of long short-term memory (LSTM) models for time series forecasting. The study aims to provide insights into the overall statistics and distribution of papers focused on LSTM for forecasting. Additionally, the research questions address the most highly cited papers based on LSTM approaches in forecasting, the most productive journals in this field, and identifying trends, gaps, summary tasks and their performance, datasets availability, and future research directions for LSTM in forecasting. This paper is a comprehensive review of LSTM for forecasting from 2017 to 2023 and identifies emerging trends, potential research gaps, and future directions for LSTM in forecasting. These findings contribute to a deeper understanding of the current state of LSTM-based forecasting research and provide valuable insights for researchers and practitioners in the field. This bibliometric and content review sheds light on the landscape of LSTM for time series forecasting, highlighting the most cited papers and productive journals and outlining potential areas for future exploration and development of LSTM models in forecasting.