How do I implement a comparison between RNNModel and BlockRNNModel

1. Import the necessary libraries:

python
   import darts
   from darts.models import RNNModel, BlockRNNModel
   from darts.metrics import mape
   

2. Load and preprocess your time series data:

python
   # Load your time series data
   df = pd.read_csv('your_data.csv')

   # Create Darts TimeSeries objects for training and testing
   train_series = TimeSeries.from_dataframe(df, time_col='timestamp', value_cols='target')
   test_series = train_series.split_before(0.8)
   

3. Define and train the RNNModel:

python
   # Define the RNNModel
   rnn_model = RNNModel(
       model="LSTM",
       hidden_dim=20,
       dropout=0.2,
       batch_size=16,
       n_epochs=100,
       optimizer_kwargs={"lr": 1e-3},
       model_name="RNN_Model",
       log_tensorboard=False,
       random_state=42
   )

   # Train the RNNModel
   rnn_model.fit(train_series)
   

4. Define and train the BlockRNNModel:

python
   # Define the BlockRNNModel
   block_rnn_model = BlockRNNModel(
       input_chunk_length=14,
       output_chunk_length=7,
       model="LSTM",
       hidden_dim=20,
       n_rnn_layers=1,
       dropout=0.2,
       batch_size=16,
       n_epochs=100,
       optimizer_kwargs={"lr": 1e-3},
       model_name="BlockRNN_Model",
       log_tensorboard=False,
       random_state=42
   )

   # Train the BlockRNNModel
   block_rnn_model.fit(train_series)
   

5. Evaluate the models on the test set:

python
   # Evaluate the RNNModel
   rnn_mape = mape(rnn_model.predict(test_series), test_series)
   print("RNNModel MAPE:", rnn_mape)

   # Evaluate the BlockRNNModel
   block_rnn_mape = mape(block_rnn_model.predict(test_series), test_series)
   print("BlockRNNModel MAPE:", block_rnn_mape)
   

6. Compare the model performance:

python
   # Compare the MAPE values
   if rnn_mape < block_rnn_mape:
       print("RNNModel performed better than BlockRNNModel.")
   else:
       print("BlockRNNModel performed better than RNNModel.")
   

This code sets up and trains both the `RNNModel` and `BlockRNNModel` on the same dataset, and then evaluates their performance on the test set using the Mean Absolute Percentage Error (MAPE) metric. You can then compare the MAPE values to determine which model performed better for your specific time series forecasting task.

Remember to adjust the hyperparameters, such as the number of epochs, batch size, and learning rate, to optimize the performance of each model for your dataset.