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Time Series Forecasting with Statistical, Machine Learning, and Deep Learning Methods: Past, Present, and Future

In: Forecasting with Artificial Intelligence

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  • Evangelos Spiliotis

    (National Technical University of Athens)

Abstract

Time series forecasting covers a wide range of methods extending from exponential smoothing and ARIMA models to sophisticated machine learning ones, such as neural networks and regression-tree-based techniques. More recently, deep learning methods have also shown considerable improvements in many forecasting applications. This chapter provides an overview of the key advances that have occurred per class of method in the last decades, presents their advantagesAdvantage and drawbacks, describes the conditions they are expected to perform better under, and discusses some approaches that can be exploited to improve their accuracy. Finally, some directions for future research are proposed to further improve their accuracy and applicability.

Suggested Citation

  • Evangelos Spiliotis, 2023. "Time Series Forecasting with Statistical, Machine Learning, and Deep Learning Methods: Past, Present, and Future," Palgrave Advances in Economics of Innovation and Technology, in: Mohsen Hamoudia & Spyros Makridakis & Evangelos Spiliotis (ed.), Forecasting with Artificial Intelligence, chapter 0, pages 49-75, Palgrave Macmillan.
    • Handle: RePEc:pal:paiecp:978-3-031-35879-1_3
    DOI: 10.1007/978-3-031-35879-1_3
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