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https://github.com/Resh-97/MixSeq-Connecting-Macroscopic-Time-Series-Forecasting-with-Microscopic-Time-Series-Data
Testing the Reproducibility of the paper: MixSeq. Under the assumption that macroscopic time series follow a mixture distribution, they hypothesise that lower variance of constituting latent mixture components could improve the estimation of macroscopic time series.
https://github.com/Resh-97/MixSeq-Connecting-Macroscopic-Time-Series-Forecasting-with-Microscopic-Time-Series-Data
arma comp6248 deepar multihead-attention reproducibility-challenge time-series vae-implementation vae-pytorch
Last synced: 15 days ago
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Testing the Reproducibility of the paper: MixSeq. Under the assumption that macroscopic time series follow a mixture distribution, they hypothesise that lower variance of constituting latent mixture components could improve the estimation of macroscopic time series.
- Host: GitHub
- URL: https://github.com/Resh-97/MixSeq-Connecting-Macroscopic-Time-Series-Forecasting-with-Microscopic-Time-Series-Data
- Owner: Resh-97
- Created: 2023-05-12T11:00:42.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-06-07T23:40:50.000Z (over 1 year ago)
- Last Synced: 2024-08-01T16:27:25.695Z (3 months ago)
- Topics: arma, comp6248, deepar, multihead-attention, reproducibility-challenge, time-series, vae-implementation, vae-pytorch
- Language: Jupyter Notebook
- Homepage:
- Size: 93.8 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0