Simplicity vs. Complexity in Time Series Forecasting: A Comparative Study of iTransformer Variants

This study re-examines the balance between architectural intricacy and generalization in Transformer models for long-term time series predictions. We perform a systematic comparison involving a lightweight baseline (iTransformer) and two enhanced versions: MiTransformer, which incorporates an external memory component for extending context, and DFiTransformer, which features dual-frequency decomposition along with Learnable Cross-Frequency Attention. All models undergo training using the same protocols across eight standard benchmarks and four forecasting periods. Findings indicate that both MiTransformer and DFiTransformer do not reliably surpass the baseline. In many instances, the increased complexity leads to greater variance and decreased accuracy, especially with unstable or inconsistent datasets. These results imply that architectural minimalism, when effectively refined, can match or surpass the effectiveness of more complex designs—challenging the prevailing trend toward increasingly intricate forecasting architectures.