Summary of Beyond Data Scarcity: a Frequency-driven Framework For Zero-shot Forecasting, by Liran Nochumsohn et al.
Beyond Data Scarcity: A Frequency-Driven Framework for Zero-Shot Forecasting
by Liran Nochumsohn, Michal Moshkovitz, Orly Avner, Dotan Di Castro, Omri Azencot
First submitted to arxiv on: 24 Nov 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 Medium Difficulty summary: This paper explores the challenges of time series forecasting when faced with scarce or nonexistent data. Traditional techniques struggle in such scenarios, motivating the development of zero-shot and few-shot learning approaches that leverage large-scale foundation models. However, these methods often require extensive data and compute resources, leading to a question about what factors influence effective learning from data. The authors propose using Fourier analysis to investigate how models learn from synthetic and real-world time series data, revealing common issues with poor learning from multi-frequency data and poor generalization to unseen frequencies. To address these problems, the researchers present a novel synthetic data generation framework called Freq-Synth, which enhances or replaces real data by creating task-specific frequency information. This approach improves the robustness of both foundation and nonfoundation forecast models in zero-shot and few-shot settings, enabling more reliable time series forecasting under limited data scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Imagine you’re trying to predict what will happen tomorrow based on what happened today. But sometimes we don’t have enough information to make a good prediction. This paper looks at how to make better predictions when we don’t have much data. They found that most forecasting methods struggle when there’s not enough data, and they came up with a new way to create fake data that helps forecasters do better. This new approach makes it possible for forecasters to make more accurate predictions even when they don’t have much information. |
Keywords
» Artificial intelligence » Few shot » Generalization » Synthetic data » Time series » Zero shot
