Hyperparameter Tuning Disasters in Price Forecasting Projects
Why Your Hyperparameter Search Produces Worse Models
Prioritize learning rate over other hyperparameters initially. Models with inappropriate learning rates never converge regardless of architecture refinements. Start with 0.001 and adjust based on training curve behavior rather than guessing.
Fix sequence length based on data characteristics before tuning other parameters. Intraday predictions need shorter windows than daily forecasts. Testing 20 different sequence lengths while using default learning rates produces meaningless comparisons.
Use learning rate schedules instead of constant values. Financial time series exhibit varying difficulty during training. Reduce learning rate when validation loss plateaus rather than training with fixed rates until arbitrary epoch limits.
Limit batch size based on available memory, not performance experiments. Larger batches provide more stable gradients but require more VRAM. Freelancers with consumer GPUs should accept batch size constraints and focus on other parameters.
Test dropout rates systematically between 0.1 and 0.5. Higher rates prevent overfitting but slow convergence. Monitor both training speed and validation performance when adjusting dropout.
Avoid tuning too many parameters simultaneously. Change one parameter at a time and document its isolated effect on model performance. Random search across six dimensions wastes computational resources.
model = Sequential([ LSTM(128, return_sequences=True), Dropout(0.2), Dense(1, activation='linear') ])