Summary of Arm: Refining Multivariate Forecasting with Adaptive Temporal-contextual Learning, by Jiecheng Lu et al.
ARM: Refining Multivariate Forecasting with Adaptive Temporal-Contextual Learning
by Jiecheng Lu, Xu Han, Shihao Yang
First submitted to arxiv on: 14 Oct 2023
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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 tackles the challenge of long-term time series forecasting (LTSF) by introducing a novel multivariate temporal-contextual adaptive learning method called ARM. The existing multivariate LTSF Transformers have been underperforming univariate models, and this is attributed to their inefficiency in modeling series-wise relationships. ARM addresses this issue by employing Adaptive Univariate Effect Learning (AUEL), Random Dropping (RD) training strategy, and Multi-kernel Local Smoothing (MKLS). These techniques enable the model to better handle individual series temporal patterns and correctly learn inter-series dependencies. The results show that ARM outperforms vanilla Transformer on multiple benchmarks without significantly increasing computational costs, making it a state-of-the-art approach for LTSF. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a big problem in forecasting future events based on past data. They found that the current methods aren’t doing well because they don’t understand how different things are related to each other over time. So, they created a new way of learning called ARM, which is better at understanding these relationships. ARM uses special techniques like learning individual patterns and smoothing out noisy data. The results show that ARM is much better than the old method on several tests, without using too many extra resources. |
Keywords
* Artificial intelligence * Time series * Transformer
