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Probabilistic AutoRegressive Neural Networks for Accurate Long-Range Forecasting

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Neural Information Processing (ICONIP 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1967))

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Abstract

Forecasting time series data is a critical area of research with applications spanning from stock prices to early epidemic prediction. While numerous statistical and machine learning methods have been proposed, real-life prediction problems often require hybrid solutions that bridge classical forecasting approaches and modern neural network models. In this study, we introduce a Probabilistic AutoRegressive Neural Network (PARNN), capable of handling complex time series data exhibiting non-stationarity, nonlinearity, non-seasonality, long-range dependence, and chaotic patterns. PARNN is constructed by improving autoregressive neural networks (ARNN) using autoregressive integrated moving average (ARIMA) feedback error. Notably, the PARNN model provides uncertainty quantification through prediction intervals and conformal predictions setting it apart from advanced deep learning tools. Through comprehensive computational experiments, we evaluate the performance of PARNN against standard statistical, machine learning, and deep learning models. Diverse real-world datasets from macroeconomics, tourism, epidemiology, and other domains are employed for short-term, medium-term, and long-term forecasting evaluations. Our results demonstrate the superiority of PARNN across various forecast horizons, surpassing the state-of-the-art forecasters. The proposed PARNN model offers a valuable hybrid solution for accurate long-range forecasting. The ability to quantify uncertainty through prediction intervals further enhances the model’s usefulness in various decision-making processes.

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Author information

Authors and Affiliations

  1. International Institute of Information Technology Bangalore, Bengaluru, India

    Madhurima Panja & Uttam Kumar

  2. Sorbonne University Abu Dhabi, Abu Dhabi, UAE

    Tanujit Chakraborty & Abdenour Hadid

Authors
  1. Madhurima Panja
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  2. Tanujit Chakraborty
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  3. Uttam Kumar
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  4. Abdenour Hadid
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Corresponding author

Correspondence to Madhurima Panja .

Editor information

Editors and Affiliations

  1. Changsha, China

    Biao Luo

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Long Cheng

  3. Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, China

    Zheng-Guang Wu

  4. School of Automation, Guangdong University of Technology, Guangdong, China

    Hongyi Li

  5. UNSW Sydney, Sydney, NSW, Australia

    Chaojie Li

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Panja, M., Chakraborty, T., Kumar, U., Hadid, A. (2024). Probabilistic AutoRegressive Neural Networks for Accurate Long-Range Forecasting. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1967. Springer, Singapore. https://doi.org/10.1007/978-981-99-8178-6_35

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  • DOI: https://doi.org/10.1007/978-981-99-8178-6_35

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  • Print ISBN: 978-981-99-8177-9

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