Simplicity vs. Complexity in time series forecasting: a comparative study of iTransformer variants

According to recent time series forecasting research, simpler models frequently perform better than their more complex counterparts, particularly over longer time horizons. By comparing two improved versions of the iTransformer—MiTransformer, which incorporates an external memory module, and DFiTransformer, which adds dual-frequency decomposition and Learnable Cross-Frequency Attention—this study investigates this assertion. Although both seek to increase forecasting accuracy, empirical findings across a number of benchmarks demonstrate that performance is frequently negatively impacted by complexity. The conclusion that simpler, well-structured architectures can provide superior generalisation and practical utility is further supported by the notable underperformance of the most complex model, DFiTransformer.