Summary of Hinge-fm2i: An Approach Using Image Inpainting For Interpolating Missing Data in Univariate Time Series, by Noufel Saad et al.
Hinge-FM2I: An Approach using Image Inpainting for Interpolating Missing Data in Univariate Time Series
by Noufel Saad, Maaroufi Nadir, Najib Mehdi, Bakhouya Mohamed
First submitted to arxiv on: 8 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 proposes a novel method called Hinge-FM2I for handling missing data values in univariate time series data. Building upon the Forecasting Method by Image Inpainting (FM2I), Hinge-FM2I uses a selection algorithm inspired by door hinges to drop data points before or after gaps and then use FM2I for imputation, selecting the imputed gap based on the lowest error of the dropped data point. The method is evaluated on a comprehensive sample of 1356 time series from the M3 competition benchmark dataset with missing value rates ranging from 3.57% to 28.57%. Experimental results show that Hinge-FM2I significantly outperforms established methods such as linear/spline interpolation, K-Nearest Neighbors (K-NN), and ARIMA, achieving an average Symmetric Mean Absolute Percentage Error (sMAPE) score of 5.6% for small gaps and up to 10% for larger ones. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make better predictions about things like traffic flow, energy usage, and patient health. But sometimes the data we use is missing pieces, which can affect how accurate our predictions are. The authors came up with a new way called Hinge-FM2I to fill in those missing gaps. It’s like using a special kind of glue that helps fix broken images. They tested it on lots of different kinds of data and found that it works better than other methods do. |
Keywords
» Artificial intelligence » Image inpainting » Time series
