A survey on uncertainty quantification in deep learning for financial time series prediction
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Other documents of the author: Blasco, Txus; Sánchez Garreta, Josep Salvador; García, Vicente
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comunitat-uji-handle2:10234/43662
comunitat-uji-handle3:10234/43643
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Title
A survey on uncertainty quantification in deep learning for financial time series predictionDate
2024-01-28Publisher
Elsevier Science DirectISSN
0925-2312; 1872-8286Bibliographic citation
Blasco, T., Sánchez, J. S., & García, V. (2024). A survey on uncertainty quantification in deep learning for financial time series prediction. Neurocomputing, 127339.Type
info:eu-repo/semantics/articleVersion
info:eu-repo/semantics/publishedVersionSubject
Abstract
Investors make decisions about buying and selling a financial asset based on available information. The traditional approach in Deep Learning when trying to predict the behavior of an asset is to take a price history, ... [+]
Investors make decisions about buying and selling a financial asset based on available information. The traditional approach in Deep Learning when trying to predict the behavior of an asset is to take a price history, train a model, and forecast one single price in the near future. This is called the frequentist perspective. Uncertainty Quantification is an alternative in which models manage a probability distribution for prediction. It provides investors with more information than the traditional frequentist way, so they can consider the risk of making or not making a certain decision. We systematically reviewed the existing literature on Uncertainty Quantification methods in Deep Learning to predict the behavior of financial assets, such as foreign exchange, stock market, cryptocurrencies and others. The article discusses types of model, categories of financial assets, prediction characteristics and types of uncertainty. We found that, in general terms, references focus on price accuracy as a metric, although other metrics, such as trend accuracy, might be more appropriate. Very few authors analyze both epistemic and aleatoric uncertainty, and none analyze in depth how to decouple them. The time period analyzed includes the years 2001 to 2022. [-]
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Neurocomputing, Vol. 576, (April 2024)Rights
info:eu-repo/semantics/openAccess
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