MR-ImagenTime: Multi-Resolution Time Series Generation through Dual Image Representations
Summary: arXiv:2603.28253v2 Announce Type: replace-cross
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.
Introduction
As industries increasingly rely on accurate time series forecasting, the demand for sophisticated models is growing. Traditional models often face limitations due to their reliance on fixed-length inputs, which can hinder their ability to adapt to varying data patterns. Additionally, the lack of multi-scale modeling can further complicate the forecasting process, leading to suboptimal results in real-world applications.
MR-CDM Framework
The MR-CDM framework addresses these issues by integrating several innovative components:
- Hierarchical Multi-Resolution Trend Decomposition: This feature allows the model to analyze data at different resolutions, capturing both short-term fluctuations and long-term trends effectively.
- Adaptive Embedding Mechanism: This mechanism is designed to handle variable-length inputs, enabling the model to adapt to the diverse nature of time series data.
- Multi-Scale Conditional Diffusion Process: This process enhances the model’s capacity to generate accurate forecasts by leveraging conditional diffusion techniques across multiple scales.
Evaluation and Results
To evaluate the performance of MR-CDM, extensive tests were conducted on four real-world datasets. The model was compared against leading baselines, including CSDI and Informer. The results were compelling, showcasing significant improvements in forecasting accuracy.
Specifically, MR-CDM demonstrated a reduction in Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) by approximately 6-10%, highlighting its effectiveness in producing reliable forecasts across different scenarios.
Conclusion
In conclusion, the MR-CDM framework represents a significant advancement in the field of time series forecasting. By addressing the limitations of existing models through its innovative components, MR-CDM not only enhances forecasting accuracy but also provides a flexible solution for diverse data inputs. The promising results from real-world evaluations underscore its potential impact across various industries reliant on time series data.