Summary of Evaluating Time-series Training Dataset Through Lens Of Spectrum in Deep State Space Models, by Sekitoshi Kanai et al.
Evaluating Time-Series Training Dataset through Lens of Spectrum in Deep State Space Models
by Sekitoshi Kanai, Yasutoshi Ida, Kazuki Adachi, Mihiro Uchida, Tsukasa Yoshida, Shin’ya Yamaguchi
First submitted to arxiv on: 29 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 This study develops a method to assess time-series datasets for their potential to perform well in deep neural networks (DNNs) with state space models (deep SSMs). Deep SSMs have gained popularity as components within DNNs for processing time-series data. However, the effectiveness of training datasets cannot be determined until they are actually used to train the DNNs. This trial-and-error approach can lead to increased costs and time-consuming processes. To facilitate the practical application of deep SSMs, this study proposes a metric that estimates performance early in the training process. The authors draw inspiration from system identification techniques, where dataset effectiveness is evaluated by analyzing the spectrum of input signals. They adapt this concept to deep SSMs, proposing the K-spectral metric, which combines the top-K spectra of signals within each layer of the DNN. Experimental results show that the K-spectral metric has a high correlation coefficient with performance and can be used to evaluate training dataset quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study is all about finding a way to predict how well deep neural networks will perform on new time-series data. These networks are really good at processing information that changes over time, like stock prices or weather patterns. But right now, we have to try different datasets and train the networks multiple times before we know which one works best. This process is slow and expensive. The researchers in this study want to change that by creating a way to estimate how well a dataset will work without having to do all that training. They looked at other techniques called system identification, where people use special signals to test how good a model is. They adapted this idea for deep neural networks and created a new metric called the K-spectral metric. This metric looks at the patterns in the data and can tell us which datasets will work best. The results show that this metric works really well! |
Keywords
» Artificial intelligence » Time series
