Architecture Selection Errors in Deep Learning Price Forecasting
Why Your Model Architecture Cannot Handle Financial Time Series
Start with sequence length requirements for your specific market. Intraday crypto predictions need different lookback windows than monthly commodity forecasts. LSTMs excel with sequences of 50-200 timesteps, but longer contexts require attention mechanisms.
Match your architecture to prediction horizon. Single-step ahead forecasting works well with encoder-decoder structures. Multi-horizon predictions need separate output heads or autoregressive approaches that acknowledge uncertainty compounds over time.
Consider whether your price series exhibits clear trends or mean reversion. Transformer models capture long-range dependencies in trending markets. Convolutional networks extract local patterns useful for high-frequency oscillations.
Evaluate computational constraints against model complexity. Training a large transformer on laptop hardware produces undertrained models worse than simpler architectures trained properly. Freelancers billing hourly cannot afford week-long training runs.
Test whether your chosen architecture handles missing data gracefully. Some structures require complete sequences while others accommodate gaps through masking. Production systems encounter data dropouts regularly.
Benchmark against simple baselines before committing to complex architectures. Linear models often outperform poorly tuned neural networks for short forecast horizons.
model = Sequential([ LSTM(128, return_sequences=True), Dropout(0.2), Dense(1, activation='linear') ])