Summary of Enhancing Foundation Models For Time Series Forecasting Via Wavelet-based Tokenization, by Luca Masserano et al.
Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization
by Luca Masserano, Abdul Fatir Ansari, Boran Han, Xiyuan Zhang, Christos Faloutsos, Michael W. Mahoney, Andrew Gordon Wilson, Youngsuk Park, Syama Rangapuram, Danielle C. Maddix, Yuyang Wang
First submitted to arxiv on: 6 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 investigates the development of foundational models for time series forecasting, focusing on tokenization as a crucial consideration. The authors propose WaveToken, a wavelet-based tokenizer that allows models to learn complex representations directly in the space of time-localized frequencies. This approach decomposes coarse and fine structures in the inputs, providing an eloquent and compact language for time series forecasting that simplifies learning. Empirical results on 42 datasets demonstrate improved accuracy, generalization capabilities, and the ability to capture complex temporal patterns. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to make better predictions about what will happen next in a sequence of things over time. They came up with a new way to split up these sequences into smaller parts that are easier for computers to understand. This helps computers learn more about the patterns in these sequences and make better guesses about what might happen next. The authors tested their approach on many different types of data and found it worked really well, even when dealing with complex patterns that other approaches struggled with. |
Keywords
» Artificial intelligence » Generalization » Time series » Tokenization » Tokenizer
