Replacing Classical Forecasting With Deep Learning Transformers
Last Updated on November 25, 2025 by Editorial Team
Author(s): Rashmi
Originally published on Towards AI.
Understanding the shift from classical ways to Transformer-based time series forecasting
Time-series forecasting has always been a critical component of finance, e-commerce, mobility, healthcare, manufacturing, and climate modeling. For decades, classical statistical models like ARIMA, SARIMA, ETS, and VAR dominated forecasting.
This article explores the transition from classical forecasting methods like ARIMA and ETS to deep learning transformers in time series forecasting. It discusses the limitations of classical models, the advantages of transformer architectures in capturing complex patterns and dependencies, use cases across different industries, and operational considerations. The piece highlights that while transformers excel in technical capabilities, classical methods remain effective for operational simplicity, particularly in instances of limited data availability.
Read the full blog for free on Medium.
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Note: Article content contains the views of the contributing authors and not Towards AI.
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