Summary of Fine-tuning a Time Series Foundation Model with Wasserstein Loss, by Andrei Chernov
Fine-Tuning a Time Series Foundation Model with Wasserstein Loss
by Andrei Chernov
First submitted to arxiv on: 18 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 Recent advancements in large language models (LLMs) for Natural Language Processing (NLP) have inspired research on developing foundational models for time series forecasting. One approach involves training LLM architectures on tokenized time series data using cross-entropy loss, which has demonstrated promising results. However, this method is primarily designed for classification tasks and does not account for the distance between classes. To address this limitation, we propose using the Wasserstein loss for such architectures. We fine-tuned a foundational time series model on 22 zero-shot datasets, comparing the performance of cross-entropy loss with that of Wasserstein loss. Our results show that replacing cross-entropy loss with Wasserstein loss significantly improves point estimation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at ways to improve computer models that predict future events based on past patterns. One approach uses big language models, which are good at understanding natural language, to forecast what might happen next in a time series (like stock prices or weather). The problem is that these models were designed for simple tasks like classifying things as “dog” or “cat”, not for forecasting complex events. To fix this, the researchers suggest using a new type of loss function called Wasserstein loss. They tested their idea on 22 different datasets and found that it really works well! By using Wasserstein loss, they were able to make more accurate predictions about what would happen next. |
Keywords
» Artificial intelligence » Classification » Cross entropy » Loss function » Natural language processing » Nlp » Time series » Zero shot
