Summary of Fate: Focal-modulated Attention Encoder For Temperature Prediction, by Tajamul Ashraf et al.
FATE: Focal-modulated Attention Encoder for Temperature Prediction
by Tajamul Ashraf, Janibul Bashir
First submitted to arxiv on: 21 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 novel FocalNet Transformer architecture, Focal modulation Attention Encoder (FATE), is introduced to improve temperature forecasting accuracy. This approach operates in a multi-tensor format, utilizing tensorized modulation to capture spatial and temporal nuances in meteorological data. The FATE framework excels at identifying complex patterns in temperature data, outperforming existing transformer encoders, 3D CNNs, LSTM, and ConvLSTM models. A new labeled dataset, the Climate Change Parameter dataset (CCPD), is presented, containing 40 years of data from Jammu and Kashmir on seven climate-related parameters. Experiments with real-world temperature datasets from the USA, Canada, and Europe show accuracy improvements of 12%, 23%, and 28%, respectively, over current state-of-the-art models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new approach for accurate temperature forecasting, which is crucial for understanding and mitigating climate change impacts. The FocalNet Transformer architecture, called FATE, uses tensorized modulation to capture spatial and temporal nuances in meteorological data. This framework outperforms existing methods, including transformer encoders, 3D CNNs, LSTM, and ConvLSTM models. A new dataset, the Climate Change Parameter dataset (CCPD), is also presented, containing 40 years of data from Jammu and Kashmir on seven climate-related parameters. |
Keywords
» Artificial intelligence » Attention » Encoder » Lstm » Temperature » Transformer
