Keras Model scenarios to Converge with Early Stopping on Time Series Data
I've been struggling with this for a few days now and could really use some help. I'm working on a time series forecasting project using TensorFlow 2.8 and Keras, but I'm struggling to get my model to converge properly. I've set up my LSTM model with two layers as follows: ```python from tensorflow.keras.models import Sequential from tensorflow.keras.layers import LSTM, Dense, Dropout model = Sequential() model.add(LSTM(50, return_sequences=True, input_shape=(timesteps, num_features))) model.add(Dropout(0.2)) model.add(LSTM(50)) model.add(Dropout(0.2)) model.add(Dense(1)) model.compile(optimizer='adam', loss='mean_squared_error') ``` I implemented early stopping to prevent overfitting: ```python from tensorflow.keras.callbacks import EarlyStopping early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True) ``` However, during training, I'm getting this warning: `UserWarning: Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss` when I try to fit the model: ```python history = model.fit(X_train, y_train, epochs=100, validation_split=0.2, callbacks=[early_stopping]) ``` The training loss decreases but the validation loss remains constant, indicating that the model isn't learning anything useful from the validation set. I have checked the data preprocessing, and I'm using MinMaxScaler to scale my features, which seems to be working fine. I've also tried adjusting the number of epochs and batch size, but it hasn't improved the convergence. Is there something I'm missing regarding validation metrics or model configuration for time series data? Should I be using a different approach for validation, like creating a custom train-test split? Any guidance would be much appreciated. For context: I'm using Python on Windows. Am I missing something obvious?