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March 3, 2025Neurocomputers

Hybrid deep learning model for time series forecasting

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Authors

AGA.V. Gulyaev

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Overview

Hybrid model improves predictive accuracy in time series forecasting, combining CNNs and RNNs with traditional methods.

Key Points

  • The hybrid model enhances forecasting accuracy by addressing complexities in time series data.
  • Experiments demonstrated that the hybrid model significantly outperforms traditional statistical methods.
  • Combining convolutional neural networks and recurrent neural networks effectively captures temporal patterns and sequences.
  • This approach highlights the potential for deep learning frameworks to surpass conventional forecasting techniques.

Cite This Study

A.V. Gulyaev (2025) studied this question.

synapsesocial.com/papers/68af7b187567bf4f94ff2a07https://doi.org/10.18127/j19998554-202503-08
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