Summary of A Temporal Linear Network For Time Series Forecasting, by Remi Genet and Hugo Inzirillo
A Temporal Linear Network for Time Series Forecasting
by Remi Genet, Hugo Inzirillo
First submitted to arxiv on: 28 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 The abstract presents a novel deep learning architecture called Temporal Linear Net (TLN) that challenges the necessity of complex models for time series forecasting. The TLN is designed to capture temporal and feature-wise dependencies in multivariate data while maintaining interpretability and efficiency. It’s a variant of TSMixer, removing activation functions and incorporating dilated convolutions to handle different time scales. Unlike transformer-based models, TLN preserves the temporal structure of the input data. A key innovation is its ability to compute an equivalent linear model, offering interpretability not found in more complex architectures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new deep learning architecture called Temporal Linear Net (TLN) that can be used for time series forecasting. The TLN is a simple and efficient way to forecast data by capturing both temporal and feature-wise dependencies in the data. It’s similar to other models like TSMixer, but it doesn’t use activation functions or have as many layers. |
Keywords
» Artificial intelligence » Deep learning » Time series » Transformer
