Unlocking Online Insights: LSTM Exploration and Transfer Learning Prospects

Muhammad Tahir, Sufyan Ali, Ayesha Sohail (), Ying Zhang and Xiaohua Jin
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Muhammad Tahir: COMSATS University Islamabad
Sufyan Ali: COMSATS University Islamabad
Ayesha Sohail: University of Sydney
Ying Zhang: The University of Sydney
Xiaohua Jin: Western Sydney University

Annals of Data Science, 2024, vol. 11, issue 4, No 13, 1434 pages

Abstract: Abstract Machine learning algorithms can improve the time series data analysis as compared to the traditional methods such as moving averages or auto-regressive approaches. This advancement has helped to unlock several challenging problems since machine learning not only helps to forecast the overall trend of the data, but it also helps to keep the historical track of changes in factors, influencing this trend. These predictions play a pivotal role in almost all areas of research where the observations are time dependent, such as problems ranging from challenges of finance to public health, environmental and climate change challenges. A key challenge of these domains is the higher number of attributes and predictors since managing and manipulating data from many attributes is itself a significant challenge for future forecasting. Addressing these challenges is possible with Recursive Long Short-Term Memory models. The application of such models is crucial, and their efficacy is further amplified when considering transfer learning. During this research, a detailed and comprehensive description of such models is addressed. Practical application is illustrated through an example, emphasizing that these models, when transferred to complex and large datasets using transfer learning, hold great promise.

Keywords: Time series forecasting; Nonlinear dynamics; Sequential and recursive networks; Climate change (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s40745-024-00551-2

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