Summary of An Analysis Of Linear Time Series Forecasting Models, by William Toner et al.
An Analysis of Linear Time Series Forecasting Models
by William Toner, Luke Darlow
First submitted to arxiv on: 21 Mar 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 paper analyzes various linear models for time series forecasting, showing that many popular variants are equivalent to standard linear regression. The authors demonstrate that each model can be reinterpreted as unconstrained linear regression over an augmented feature set, allowing for closed-form solutions with a mean-squared loss function. They provide experimental evidence that the models learn similar solutions and show that simpler closed-form solutions outperform more complex ones in 72% of test settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study shows that simple linear models are effective at predicting future values in time series data, even when compared to more complex models. By looking at different versions of these linear models, the researchers found that many of them are actually doing the same thing as a standard linear regression model. They also showed that this simpler approach can be just as good or better than more complicated methods for forecasting. |
Keywords
* Artificial intelligence * Linear regression * Loss function * Time series
