Summary of Knowledge-enhanced Transformer For Multivariate Long Sequence Time-series Forecasting, by Shubham Tanaji Kakde et al.
Knowledge-enhanced Transformer for Multivariate Long Sequence Time-series Forecasting
by Shubham Tanaji Kakde, Rony Mitra, Jasashwi Mandal, Manoj Kumar Tiwari
First submitted to arxiv on: 17 Nov 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 This paper proposes a novel approach to multivariate long sequence time-series forecasting (LSTF) by integrating knowledge graph embeddings (KGEs) with transformer-based architectures. The authors aim to capture complex temporal and relational dynamics across multiple domains by incorporating conceptual relationships among variables within a well-defined knowledge graph. They investigate the influence of this integration into seminal architectures such as PatchTST, Autoformer, Informer, and Vanilla Transformer, demonstrating significant improvement in benchmark results for long forecasting horizons. This enhancement empowers transformer-based architectures to address the inherent structural relation between variables, improving the accuracy of multivariate LSTF. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to predict what will happen in the future based on past data. It’s like trying to guess the weather tomorrow by looking at yesterday’s weather patterns. The researchers used special computer models called transformers that are good at understanding patterns in time series data, like temperature readings or weather forecasts. But they wanted to make these models even better by giving them more information about how different variables relate to each other. They created a new way of doing this using something called knowledge graph embeddings. This allowed their models to capture complex relationships between variables and make more accurate predictions for longer time periods. The results show that this approach can improve forecasting accuracy, which is important for many real-world applications. |
Keywords
» Artificial intelligence » Knowledge graph » Temperature » Time series » Transformer
