This page organizes papers related to artificial intelligence published around the world. This page is summarized using Google Gemini and is operated on a non-profit basis. The copyright of the paper belongs to the author and the relevant institution. When sharing, simply cite the source.
KAIROS is a non-autoregressive time series forecasting framework developed to support real-time decision-making in web applications. It directly models segment-level multi-peak distributions, prevents error accumulation, and performs inference immediately. Trained on a large dataset, KAIROS improves forecasting performance over existing non-autoregressive models. KAIROS demonstrates strong zero-shot generalization across six benchmarks, offering performance comparable to existing state-of-the-art models at a lower inference cost.
Takeaways, Limitations
•
Takeaways:
◦
Providing reliable time series forecasts for efficient operation of web applications.
◦
Instant inference for real-time decision making, without error accumulation.
◦
Improved prediction performance compared to existing models.
◦
Applicability to various scenarios through zero-shot generalization performance.
◦
Emphasizes the importance of non-autoregressive designs in the field of time series forecasting.
•
Limitations:
◦
The specific Limitations is not specified in the paper.
◦
Although the models being compared are similar in size, there is little information on what types of datasets KAIROS is vulnerable to.
