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New Q1 paper: Classical to Generative AI for Time Series Forecasting (Finance, Energy, Health)

I’m pleased to share our new Q1 Scopus-indexed article in Discover Artificial Intelligence on time series forecasting across finance, energy, and health, comparing classical, ML, DL, and generative AI methods.
Go to the profile of Hassan Oukhouya
Hassan Oukhouya
Assistant Professor, Mohammed First University of Oujda
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Springer International Publishing
Springer International Publishing Springer International Publishing

From classical to generative AI approaches for univariate and multivariate time series forecasting with an evaluation in finance, energy, and health domains

Time series forecasting plays a central role in finance, energy, and public health. Classical statistical, machine learning, deep learning, and generative approaches have all been applied to forecasting tasks in these fields, but comparisons between them are usually confined to a single domain or to models from the same family, and few studies report both univariate and multivariate results under the same conditions. This paper presents a controlled cross-domain comparison of four representative paradigms: classical statistics (Seasonal Autoregressive Integrated Moving Average with Exogenous variables, SARIMAX), gradient boosting machine learning (Light Gradient Boosting Machine, LightGBM), recurrent deep learning (Recurrent Neural Network, RNN), and generative-adversarial deep learning (Conditional Generative Adversarial Network, CGAN). Each model is evaluated on three monthly datasets with contrasting characteristics: Bitcoin prices (175 observations, high volatility), U.S. energy consumption (612 observations, strong seasonality), and U.S. cardiovascular mortality (300 observations, gradual trend with pandemic shock). Both univariate and multivariate variants are tested under the same preprocessing and one-step-ahead evaluation protocols, using eight performance metrics. The CGAN reaches the lowest MAPE on energy consumption (2.88%). On Bitcoin, the multivariate LightGBM lowers the MAPE from 28.26 to 19.25%, while on cardiovascular mortality the RNN reaches 3.34% MAPE. No paradigm performs best in every domain, and the gain from exogenous variables depends on both the paradigm and the domain.

I am glad to share the publication of our new research article with my co-authors: Mohamed Nachat, Said El Melhaoui, Moustapha Faizi, Mohamed Fihri, Raby Guerbaz, and Salah-Eddine El Adlouni:

“From classical to generative AI approaches for univariate and multivariate time series forecasting with an evaluation in finance, energy, and health domains”
Published in Discover Artificial Intelligence (Q1, Scopus-indexed).

The study provides a cross-domain comparison of classical statistical methods, machine learning, deep learning, and generative AI approaches for time series forecasting across finance, energy, and health applications.

As a co-supervisor, I am particularly proud of our PhD student Mohamed Nachat for this excellent work and his valuable contribution to the research. Congratulations, Mohamed — great work, and keep going! The best is yet to come.

Many thanks to all my co-authors for this fruitful collaboration.

DOI: https://doi.org/10.1007/s44163-026-01866-0
ResearchGate: https://www.researchgate.net/publication/413555081_From_classical_to_generative_AI_approaches_for_univariate_and_multivariate_time_series_forecasting_with_an_evaluation_in_finance_energy_and_health_domains

Go to the profile of Hassan Oukhouya
Hassan Oukhouya (He/Him)
Assistant Professor, Mohammed First University of Oujda

I am an Assistant Professor of Statistics and Applied Mathematics at Mohammed First University of Oujda, Morocco. My research focuses on statistical modeling, econometrics, and financial time series forecasting, with interests in stochastic processes, machine learning, deep learning, and generative models. I publish in peer-reviewed journals indexed in Scopus and Web of Science, combining theoretical and applied research in finance, insurance, Healthcare, Social Sciences, and data analytics.

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