Summary of A Novel Hyperdimensional Computing Framework For Online Time Series Forecasting on the Edge, by Mohamed Mejri et al.
A Novel Hyperdimensional Computing Framework for Online Time Series Forecasting on the Edge
by Mohamed Mejri, Chandramouli Amarnath, Abhijit Chatterjee
First submitted to arxiv on: 3 Feb 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 framework, TSF-HD, for online time-series forecasting that adapts to changes in the data distribution. By reframing the problem as linear hyperdimensional forecasting, the authors develop an efficient and lightweight approach that outperforms state-of-the-art methods while reducing inference latency. The framework uses a co-training scheme to update its hyperdimensional mapping and predictor, allowing it to effectively handle nonlinear low-dimensional time-series data. TSF-HD is shown to perform well for both short-term and long-term forecasting tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better predict future events based on past patterns. Right now, there are two main ways to do this: offline models that can’t adapt to changes in the data, and online models that are complex and slow. The authors of this paper came up with a new way called TSF-HD that takes a different approach by looking at the data in a higher dimension. This lets them make predictions quickly and accurately, even when the patterns change. They tested it on some real-world problems and found it worked really well. |
Keywords
* Artificial intelligence * Inference * Time series
