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

Evangelos Spiliotis ()
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Evangelos Spiliotis: National Technical University of Athens

Chapter Chapter 3 in Forecasting with Artificial Intelligence, 2023, pp 49-75 from Palgrave Macmillan

Abstract: 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.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:pal:paiecp:978-3-031-35879-1_3

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DOI: 10.1007/978-3-031-35879-1_3

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