Summary of Tripcast: Pre-training Of Masked 2d Transformers For Trip Time Series Forecasting, by Yuhua Liao et al.
TripCast: Pre-training of Masked 2D Transformers for Trip Time Series Forecasting
by Yuhua Liao, Zetian Wang, Peng Wei, Qiangqiang Nie, Zhenhua Zhang
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel deep learning approach, called TripCast, is proposed to address the unique challenges of time series forecasting in the tourism industry. The approach treats trip data as 2D data, leveraging masking and reconstruction processes to learn representations. Pre-trained on large-scale real-world data, TripCast outperforms state-of-the-art baselines in in-domain forecasting scenarios, demonstrating strong scalability and transferability in out-domain scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way of using deep learning is developed to predict future tourism patterns. This approach considers trip information as a 2D puzzle and uses clever tricks to learn patterns from large amounts of real-world data. The result is a model that does better than other methods at predicting what will happen in the future, both in similar situations and in new ones. |
Keywords
» Artificial intelligence » Deep learning » Time series » Transferability
