Summary of Neural Fourier Modelling: a Highly Compact Approach to Time-series Analysis, by Minjung Kim et al.
Neural Fourier Modelling: A Highly Compact Approach to Time-Series Analysis
by Minjung Kim, Yusuke Hioka, Michael Witbrock
First submitted to arxiv on: 7 Oct 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 approach to neural time-series analysis, focusing on modeling data directly in the Fourier domain rather than the traditional time domain. The authors introduce Neural Fourier Modelling (NFM), which leverages two key properties of the Fourier transform: treating finite-length time series as continuous-time elements and manipulating data within the Fourier domain through frequency extrapolation and interpolation. To facilitate compact and expressive modeling, NFM incorporates Learnable Frequency Tokens (LFT) and Implicit Neural Fourier Filters (INFF). The authors demonstrate state-of-the-art performance on a range of tasks, including forecasting, anomaly detection, and classification, using a wide variety of time-series data with varying lengths and sampling rates. This approach requires fewer than 40K parameters per task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to analyze time-series data using the Fourier transform. Instead of looking at the data over time, they look at it in terms of frequency. They create a special model called NFM that can do this and learn from the data. It’s like a shortcut that makes the model more compact and efficient. The authors test their approach on different kinds of tasks and show that it works really well. This is important because time-series data is used in many areas, such as predicting weather or stock prices. |
Keywords
» Artificial intelligence » Anomaly detection » Classification » Time series
