MR-ImagenTime: Multi-Resolution Time Series Generation through Dual Image Representations

• URL: http://arxiv.org/abs/2603.28253v2
• Date: Wed, 08 Apr 2026 03:27:07 GMT
• Title: MR-ImagenTime: Multi-Resolution Time Series Generation through Dual Image Representations
• Abstract summary: Time series forecasting is vital across many domains, yet existing models struggle with fixed-length inputs and inadequate multi-scale modeling.<n>We propose MR-CDM, a framework combining hierarchical multi-resolution trend decomposition, an adaptive embedding mechanism for variable-length inputs, and a multi-scale conditional diffusion process.
• Score: 3.2470274231033502
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Time series forecasting is vital across many domains, yet existing models struggle with fixed-length inputs and inadequate multi-scale modeling. We propose MR-CDM, a framework combining hierarchical multi-resolution trend decomposition, an adaptive embedding mechanism for variable-length inputs, and a multi-scale conditional diffusion process. Evaluations on four real-world datasets demonstrate that MR-CDM significantly outperforms state-of-the-art baselines (e.g., CSDI, Informer), reducing MAE and RMSE by approximately 6-10 to a certain degree.

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