The article evaluates seven baseline models, including N-BEATS, Autoformer, Informer, Reformer, LogTrans, LSTNet, and LSTM. These models serve as a benchmark to compare the performance of the proposed transformer architecture.
Hyperparameters
The authors discuss various hyperparameter settings for the transformer model, including the number of encoder layers, the number of decoder layers, the embedding dimension, and the hidden size. They emphasize the importance of finding the optimal hyperparameter setting to achieve better performance.
Context: Acknowledgments
The article acknowledges the support received from various institutions, including the National Research Foundation of Korea, the Brain Convergence Research Program, and Samsung Electronics Co., Ltd. These funding agencies have contributed significantly to the development of transformer-based models for time series forecasting.
Conclusion
In conclusion, this article provides a comprehensive guide to transformer architectures for time series forecasting. By demystifying complex concepts and providing an in-depth analysis of baseline models, hyperparameter settings, and acknowledgments, this summary aims to serve as a valuable resource for researchers and practitioners working in this field. The authors’ proposed model has shown promising results, and with further optimization and refinement, it has the potential to revolutionize the way we approach time series forecasting.
