Summary of Conditional Denoising Meets Polynomial Modeling: a Flexible Decoupled Framework For Time Series Forecasting, by Jintao Zhang et al.
Conditional Denoising Meets Polynomial Modeling: A Flexible Decoupled Framework for Time Series Forecasting
by Jintao Zhang, Mingyue Cheng, Xiaoyu Tao, Zhiding Liu, Daoyu Wang
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel approach to time series forecasting, addressing the limitations of existing methods by decomposing complex temporal patterns into trend and seasonal components. The Conditional Denoising Polynomial Modeling (CDPM) framework combines probabilistic diffusion models with deterministic linear models, trained end-to-end for enhanced modeling capabilities. The framework captures fluctuating seasonal components using probabilistic diffusion models based on historical statistical properties and smooth trends utilizing modules that incorporate historical dependencies to mitigate noise distortion. Experimental results on six benchmarks demonstrate the effectiveness of CDPM, highlighting its potential for combining probabilistic and deterministic models in time series forecasting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making better predictions about what will happen next in a sequence of events over time. It’s really important because this type of prediction is used in many areas like business, finance, and weather forecasting. The problem with current methods is that they don’t separate out the different patterns in the data, like big trends and smaller fluctuations. This new approach breaks down the patterns into trend and seasonal components and uses a combination of statistical models to make more accurate predictions. |
Keywords
» Artificial intelligence » Time series
