Summary of Addressing Prediction Delays in Time Series Forecasting: a Continuous Gru Approach with Derivative Regularization, by Sheo Yon Jhin et al.
Addressing Prediction Delays in Time Series Forecasting: A Continuous GRU Approach with Derivative Regularization
by Sheo Yon Jhin, Seojin Kim, Noseong Park
First submitted to arxiv on: 29 Jun 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 Time series forecasting is crucial for various applications, including economic analysis, meteorology, and more. Traditional models are trained using mean squared error (MSE), but this approach suffers from prediction delay, where predictions arrive after the ground-truth. This limitation can have severe consequences in fields like finance and weather forecasting, making post-observation predictions meaningless despite low MSEs. To address this issue, our paper proposes a novel solution, introducing a continuous-time gated recurrent unit (GRU) based on neural ordinary differential equations (NODE). We generalize the GRU architecture to minimize prediction delay through time-derivative regularization. Our method outperforms in metrics like MSE, Dynamic Time Warping (DTW), and Time Distortion Index (TDI). We demonstrate its effectiveness in various datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Time series forecasting is important for many fields, such as predicting the weather or economy. Traditional methods have a big problem: they make predictions after what actually happened. This can cause big issues, especially in areas like finance. Our research solves this problem by creating a new kind of model that doesn’t have this issue. We show that our model is better than others at making accurate predictions and reducing delays. |
Keywords
* Artificial intelligence * Mse * Regularization * Time series
