Summary of Towards Invariant Time Series Forecasting in Smart Cities, by Ziyi Zhang et al.
Towards Invariant Time Series Forecasting in Smart Cities
by Ziyi Zhang, Shaogang Ren, Xiaoning Qian, Nick Duffield
First submitted to arxiv on: 8 May 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 This paper proposes a novel solution for improving time series forecasting in the context of smart cities, where deep neural networks have shown promising results but struggle with generalizing to out-of-distribution data. The authors aim to tackle this challenge by deriving invariant representations that enable robust predictions across different urban environments. Their method is tested on both synthetic and real-world datasets, demonstrating superior performance compared to traditional time series forecasting models when handling domain shifts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a smart city, predicting what will happen next in the world of data is crucial for planning and making good decisions. Right now, computers are really good at looking into the future, but they can get stuck if the data changes unexpectedly. The problem is that cities are all different, so what works well in one city might not work as well in another. To solve this problem, scientists developed a new way to make predictions that works no matter where you are in the city. They tested it with real data and found that it was much better than older methods at predicting what would happen next. |
Keywords
» Artificial intelligence » Time series
