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timeseries--cnn

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imehranasgari / DL_TimeSeries_Transformer_TCN_RandomData

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This project implements a Temporal Convolutional Network (TCN) for time series forecasting. Using synthetic data (including trend, seasonality, and noise), the model’s ability to learn complex patterns and provide probabilistic forecasts is demonstrated with the Darts library.

python timeseries deep-learning time-series interactive numpy pytorch transformer forecasting randomdata tcn timeseries-rnn-recurrent-neural-network timeseries--cnn
  • Updated Aug 17, 2025
  • Jupyter Notebook

imehranasgari / DL_TimeSeries_Transformer_TCN_yfinance_01

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This project forecasts MSFT stock prices by comparing four advanced deep learning models: TFT, TCN, DeepAR, and N-BEATS. It uses a robust pipeline with technical indicators as features. The TCN model achieved the highest accuracy, demonstrating a comprehensive approach to time-series model selection.

python benchmark timeseries deep-learning time-series interactive numpy end-to-end pandas pytorch transformer lstm forecasting rnn matplotlib tcn yfinance timeseries-rnn-recurrent-neural-network timeseries--cnn
  • Updated Aug 19, 2025
  • Jupyter Notebook

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