Highlights
- Pro
Pinned Loading
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Interpretable-LSTM
Interpretable-LSTM PublicInterpreting the outputs of an LSTM using the Captum library. Explores various methods such as DeepLIFT and integrated gradients to assign attribution scores to features.
Jupyter Notebook 1
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LTSM-Stock-Price-Prediction
LTSM-Stock-Price-Prediction PublicUsing an LSTM built with PyTorch to predict time series of stock prices. Testing out different loss functions and optimizing hyperparameters using Ray Tune.
Jupyter Notebook 1
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Novel-Time-Series-Models
Novel-Time-Series-Models PublicExploring various novel model architectures (such as TimesNet, N-BEATS, and N-HiTS) for time series forecasting of stock prices. Tests the effect of past and future exogenous variables on predictio…
Jupyter Notebook 1
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Optimized-Timeseries-Autoencoder
Optimized-Timeseries-Autoencoder PublicBuild an autoencoder using LSTM layers to embed time series sequences into a lower-dimensional representation. The embedding size is optimized using Ray.
Jupyter Notebook
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PyTorchDTW
PyTorchDTW PublicImplements the classical DTW algorithm in PyTorch, enabling multi-pattern matching and GPU acceleration
Jupyter Notebook 1
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