Soft computing hybrids for FOREX rate prediction: A comprehensive review
By D. Pradeepkumar, Vadlamani Ravi. 2018
69 citations recorded by the source corpus.
Abstract Foreign exchange rate prediction is an important problem in finance and it attracts many researchers owing to its complex nature and practical applications. Even though this problem is well studied using various statistical and machine learning techniques in stand-alone mode, various soft computing hybrids were also proposed to solve this problem with the aim of obtaining more accurate predictions during 1998–2017. This paper presents a comprehensive review of 82 such soft computing hybrids found in the literature. Almost all authors in this area demonstrated that their proposed hybrids outperformed the stand-alone statistical and intelligent techniques in terms of accuracy. It is conspicuous from the review that artificial neural network based hybrids turned out to be more prevalent, more pervasive and more powerful. This observation is corroborated by the fact that both evolutionary computation based hybrids as well as fuzzy logic based hybrids also contained some architecture of neural networks as a predominant constituent. The review concludes with a set of insightful remarks and future directions that are very much useful to budding researchers and practitioners alike.
Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Management Science and Operations Research, Economics & Operations Research, Time-Series & Forecasting
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Citations in the corpus
- Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (2019, methodology): “In a different time series forecasting application, researchers surveyed forex prediction studies using ANN [35] and various other soft computing techniques [36].”