Summary of Fast Gibbs Sampling For the Local and Global Trend Bayesian Exponential Smoothing Model, by Xueying Long et al.
Fast Gibbs sampling for the local and global trend Bayesian exponential smoothing model
by Xueying Long, Daniel F. Schmidt, Christoph Bergmeir, Slawek Smyl
First submitted to arxiv on: 29 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation (stat.CO); Machine Learning (stat.ML)
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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 A novel Bayesian exponential smoothing (BES) model is introduced that effectively captures strong trends and volatility in time series data. The proposed method, which builds upon Smyl et al.’s work, achieves state-of-the-art performance in various forecasting tasks. However, its original fitting procedure based on the NUTS sampler is computationally expensive, limiting its practical applicability. To address this challenge, the authors propose several modifications to the model and develop a bespoke Gibbs sampler for posterior exploration, resulting in a significant reduction of sampling time by an order of magnitude. The new BES model and sampler are evaluated on the M3 dataset, demonstrating competitive or superior accuracy compared to the original method while being much faster to run. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to forecast future events using data about past trends. It’s like trying to predict what will happen next in a series of numbers that repeats itself over time. The current method is really good at doing this, but it takes a long time to get the answer. To make it faster, the authors changed some parts of the original method and created a new way to look at the data. They tested their new method on a big dataset and found that it was just as good or even better than the old method, while being much quicker. |
Keywords
» Artificial intelligence » Time series
