Summary of Mixlinear: Extreme Low Resource Multivariate Time Series Forecasting with 0.1k Parameters, by Aitian Ma et al.
MixLinear: Extreme Low Resource Multivariate Time Series Forecasting with 0.1K Parameters
by Aitian Ma, Dongsheng Luo, Mo Sha
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed MixLinear model is an ultra-lightweight multivariate time series forecasting solution designed for resource-constrained devices. It leverages both temporal and frequency domain features by modeling intra-segment and inter-segment variations in the time domain, while extracting frequency variations from a low-dimensional latent space in the frequency domain. This approach reduces the parameter scale of a downsampled n-length input/output one-layer linear model from O(n^2) to O(n), achieving efficient computation without sacrificing accuracy. MixLinear’s performance is comparable to or surpasses state-of-the-art models with significantly fewer parameters (0.1K), making it well-suited for deployment on devices with limited computational capacity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MixLinear is a new way to forecast the future using old data. It helps make predictions about long-term events by looking at patterns and trends in historical data. The problem is that current methods are too complex and take up too much computer power. MixLinear solves this by being very lightweight and efficient, while still making accurate predictions. This means it can be used on devices with limited computing power, like a smartwatch or a smartphone. |
Keywords
» Artificial intelligence » Latent space » Time series
