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<p>Time series forecasting plays a critical role in decision-making across diverse domains such as finance, healthcare, and environmental monitoring. While classical statistical models like ARIMA remain interpretable and efficient, they often struggle with nonlinear patterns and dynamic dependencies. This review systematically examines how artificial intelligence (AI) and optimization techniques enhance forecasting accuracy and robustness. We evaluate modern deep learning architectures (e.g., LSTM, GRU, Transformers), hybrid frameworks (e.g., VMD-LSTM, CNN-GRU), and optimization-augmented models. A meta-analysis of over 150 studies reveals that deep learning-based approaches, particularly those enhanced with Adam and RMSProp optimizers, improve forecasting accuracy by up to 14% compared to traditional methods. Hybrid models demonstrate superior performance in multi-step predictions and handling volatility. The analysis includes financial datasets (S&amp;P 500, NASDAQ) and environmental data (Beijing <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(PM_{2.5}\)</EquationSource> </InlineEquation>). Despite their power, AI-driven models face challenges including interpretability, computational cost, and data dependency. Future directions highlight explainable AI, transfer learning, and lightweight architectures to address these limitations. This work serves as a reference for researchers exploring the evolving landscape of time series forecasting through AI and optimization integration.</p>

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Artificial intelligence and classical statistical models for time series forecasting: a comprehensive review

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月4日

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  • Essam H. Houssein,
  • Meran Mohamed,
  • Eman M. G. Younis,
  • Waleed M. Mohamed

摘要

Time series forecasting plays a critical role in decision-making across diverse domains such as finance, healthcare, and environmental monitoring. While classical statistical models like ARIMA remain interpretable and efficient, they often struggle with nonlinear patterns and dynamic dependencies. This review systematically examines how artificial intelligence (AI) and optimization techniques enhance forecasting accuracy and robustness. We evaluate modern deep learning architectures (e.g., LSTM, GRU, Transformers), hybrid frameworks (e.g., VMD-LSTM, CNN-GRU), and optimization-augmented models. A meta-analysis of over 150 studies reveals that deep learning-based approaches, particularly those enhanced with Adam and RMSProp optimizers, improve forecasting accuracy by up to 14% compared to traditional methods. Hybrid models demonstrate superior performance in multi-step predictions and handling volatility. The analysis includes financial datasets (S&P 500, NASDAQ) and environmental data (Beijing \(PM_{2.5}\) ). Despite their power, AI-driven models face challenges including interpretability, computational cost, and data dependency. Future directions highlight explainable AI, transfer learning, and lightweight architectures to address these limitations. This work serves as a reference for researchers exploring the evolving landscape of time series forecasting through AI and optimization integration.