Summary of Apollo-forecast: Overcoming Aliasing and Inference Speed Challenges in Language Models For Time Series Forecasting, by Tianyi Yin et al.
Apollo-Forecast: Overcoming Aliasing and Inference Speed Challenges in Language Models for Time Series Forecasting
by Tianyi Yin, Jingwei Wang, Yunlong Ma, Han Wang, Chenze Wang, Yukai Zhao, Min Liu, Weiming Shen, Yufeng Chen
First submitted to arxiv on: 16 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper introduces Apollo-Forecast, a novel framework for time series forecasting that tackles challenges like aliasing distortion and prolonged inference times using two key innovations: the Anti-Aliasing Quantization Module (AAQM) and the Race Decoding (RD) technique. The AAQM encodes sequences into tokens while mitigating high-frequency noise in the original signals, enhancing signal fidelity and quantization efficiency. The RD employs a draft model for parallel processing and result integration, accelerating inference speed for long-term predictions. Experiments on real-world datasets show Apollo-Forecast outperforms state-of-the-art methods by 35.41% and 18.99% in WQL and MASE metrics respectively, with a 1.9X-2.7X acceleration in inference speed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to forecast future events based on past data. It solves some problems that previous models had, like losing important details and taking too long to make predictions. The new approach is called Apollo-Forecast and it uses two special tricks: AAQM (Anti-Aliasing Quantization Module) and RD (Race Decoding). AAQM helps keep the important information in the data and makes sure the forecast is accurate. RD lets the computer do multiple forecasts at the same time, which makes it much faster. The paper tested Apollo-Forecast on real-world data and found that it did better than other methods by a lot. |
Keywords
* Artificial intelligence * Inference * Quantization * Time series
