PETformer: Long-term Time Series Forecasting via Placeholder-enhanced Transformer

Understand

Recently, the superiority of Transformer for long-term time series forecasting (LTSF) tasks has been challenged, particularly since recent work has shown that simple models can outperform numerous Transformer-based approaches.

• This suggests that a notable gap remains in fully leveraging the potential of Transformer in LTSF tasks.
• Consequently, this study investigates key issues when applying Transformer to LTSF, encompassing aspects of temporal continuity, information density, and multi-channel relationships.
• We introduce the Placeholder-enhanced Technique (PET) to enhance the computational efficiency and predictive accuracy of Transformer in LTSF tasks.