Forthcoming Articles

International Journal of Computational Intelligence Studies

International Journal of Computational Intelligence Studies (IJCIStudies)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Computational Intelligence Studies (One paper in press)

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  • Beyond sequential prediction: learning financial market dynamics in volatile and non-stationary environments through sentiment-conditioned generative modelling   Order a copy of this article
    by Alexis Lazanas, Spyridon Karpouzis 
    Abstract: The problem of time-series forecasting in non-stationary and complex environments is a challenging task in machine learning, especially with heterogeneous numerical and textual data present. Traditional statistical models like autoregressive integrated moving average (ARIMA) are based on the assumptions of linearity and stationarity, whereas recurrent neural networks like long short-term memory (LSTM) models do not necessarily represent distributional properties in highly volatile settings. This paper proposes a hybrid model that combines generative adversarial networks (GANs) with natural language processing (NLP)-based sentiment analysis to enable sentiment-conditioned time-series prediction. The model integrates adversarial learning on numerical sequences with contextual sentiment representations derived from unstructured text, enabling them to be jointly modelled to capture temporal dynamics and exogenous information. These results demonstrate the promise of hybrid generative and language-aware methods to enhance prediction robustness in non-stationary environments.
    Keywords: computational intelligence; generative models; sentiment analysis; financial time-series forecasting; volatile and non-stationary environments.
    DOI: 10.1504/IJCISTUDIES.2026.10079804
     

 

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