How can I use Keras to train a model with a time-series dataset using GRU layers for better accuracy

Question

Can i know How can I use Keras to train a model with a time-series dataset using GRU layers for better accuracy?

Answer 1

Use tf.keras.layers.GRU with proper preprocessing, feature scaling, and learning rate scheduling to improve accuracy in time-series forecasting.

Here is the code snippet you can refer to:

In the above code, we are using the following approaches:

• Captures Temporal Dependencies: GRU retains past information without vanishing gradients.
• Faster than LSTM: Fewer parameters, making it computationally efficient.
• Dropout for Regularization: Reduces overfitting in sequential data.
• Scalability: Works well with multivariate time-series inputs.
• Flexible Output: Can be used for forecasting (single value) or sequence prediction (multiple steps).

Hence, GRU-based models in Keras are highly effective for time-series forecasting, offering improved accuracy with proper preprocessing, tuning, and regularization techniques.