Summary of Continuous-time Linear Positional Embedding For Irregular Time Series Forecasting, by Byunghyun Kim and Jae-gil Lee
Continuous-Time Linear Positional Embedding for Irregular Time Series Forecasting
by Byunghyun Kim, Jae-Gil Lee
First submitted to arxiv on: 30 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 In this paper, researchers tackle the challenge of forecasting irregularly sampled time series data, which is common in practical applications but has received limited attention from previous research. The authors propose a method called CTLPE (Continuous Temporal Linear Positional Embedding) to extend transformer architectures to handle irregular time series. By learning a continuous linear function to encode temporal information, the model addresses two key challenges: inconsistent observation patterns and irregular time gaps. Experimental results show that CTLPE outperforms existing techniques across various irregularly-sampled time series datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about predicting future values in data that comes at different times. Right now, most forecasting methods only work for data where the time between measurements is always the same. But real-life data often doesn’t fit this pattern. To fix this problem, the authors developed a new method called CTLPE (Continuous Temporal Linear Positional Embedding). It uses a special kind of math to understand the patterns in the data and make better predictions. |
Keywords
» Artificial intelligence » Attention » Embedding » Time series » Transformer
