关键词: 分段迭代, 变长时间序列预测, 循环神经网络, 时间序列, 深度学习

Abstract: To address the challenges of high complexity, information loss, and error accumulation in existing models based on Recurrent Neural Network (RNN) for time series forecasting tasks, along with the difficulty current forecasting models face in adapting to dynamically changing data lengths, a novel variable-length time series forecasting model based on segmented iteration, named PatchRNN was proposed. A subsequence segmentation approach is employed to divide time series data into multiple relatively short subsequence segments and Gated Recurrent units are utilized for iterative processing, effectively reducing the number of iterations required by RNN models and significantly improving forecasting accuracy. Meanwhile, through segmented iteration of these subsequence segments, the proposed model can achieve variable-length time series forecasting without requiring retraining, enhancing model’s adaptability for various applications. The experimental results demonstrate that, compared with several advanced baseline models including iTransformer and TimesNet, the proposed model maintains fewer parameters and achieves reductions in Mean Squared Error (MSE) by 7.3%-45.2% and Mean Absolute Error (MAE) by 3.3%-34.2% on long-term time series forecasting tasks across four datasets, including power transformer temperature and exchange rates, which confirms its superior forecasting accuracy and computational efficiency.

Key words: segmented iteration, variable-length time series forecasting, recurrent neural network, time series, deep learning