iTransformer rethinks the transformer architecture by inverting the attention mechanism to focus on temporal variables rather than time steps, achieving state-of-the-art results on major time series forecasting benchmarks. The implementation provides training, evaluation, and inference code along with pre-trained models, and has been adopted into production ML frameworks like GluonTS and NeuralForecast.