Summary of Wave-mask/mix: Exploring Wavelet-based Augmentations For Time Series Forecasting, by Dona Arabi et al.
Wave-Mask/Mix: Exploring Wavelet-Based Augmentations for Time Series Forecasting
by Dona Arabi, Jafar Bakhshaliyev, Ayse Coskuner, Kiran Madhusudhanan, Kami Serdar Uckardes
First submitted to arxiv on: 20 Aug 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 A novel data augmentation approach is presented for improving machine learning model performance in time series forecasting (TSF) tasks. Traditional augmentation methods are inadequate when considering the temporal coherence requirements of TSF applications like finance, healthcare, and manufacturing. The proposed Wavelet Masking (WaveMask) and Wavelet Mixing (WaveMix) techniques leverage the discrete wavelet transform (DWT) to modify frequency components while preserving temporal dependencies in time series data. Experimental results demonstrate that these methods achieve competitive performance with previous approaches across various forecasting horizons. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make better predictions about what will happen next in a series of numbers, like stock prices or weather patterns. Right now, we don’t have enough real-world data to train our machines learning models, so we use fake data to help them learn. But this fake data isn’t always good at keeping the same pattern as the real data, which is important for predicting what will happen next. The researchers came up with a new way to make fake data that keeps the same pattern as the real data by using something called the discrete wavelet transform (DWT). They tested their method on different types of forecasting tasks and found it works well. |
Keywords
» Artificial intelligence » Data augmentation » Machine learning » Time series
