EXCODER: EXplainable Classification Of DiscretE time series Representations | ScienceToStartup | ScienceToStartup

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References (48)

[1]

Quality Prediction in Arc Welding: Leveraging Transformer Models and Discrete Representations from Vector Quantised-VAE

2024Yannik Hahn, Robert F. Maack et al.

[2]

A Time Series is Worth 64 Words: Long-term Forecasting with Transformers

2022Yuqi Nie, Nam H. Nguyen et al.

[3]

TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis

Founder's Pitch

"Develop an explainable AI tool for time series analysis using discrete latent representations for enhanced interpretability."

Explainable AI for Time Series•Score: 5•View PDF ↗

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0-10 scale

High Potential

1/4 signals

2.5

Quick Build

3/4 signals

7.5

Series A Potential

2/4 signals

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EXCODER: EXplainable Classification Of DiscretE time series Representations

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References (48)

Founder's Pitch

"Develop an explainable AI tool for time series analysis using discrete latent representations for enhanced interpretability."

Commercial Viability Breakdown

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EXCODER: EXplainable Classification Of DiscretE time series Representations

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References (48)

Founder's Pitch

"Develop an explainable AI tool for time series analysis using discrete latent representations for enhanced interpretability."

Commercial Viability Breakdown

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