Deep Neural Networks for Time Series

Understanding Deep Neural Networks for Time Series

This visualization demonstrates how Deep Neural Networks (DNNs) learn patterns in time series data using the classic airline passenger dataset (1949-1960). DNNs use multiple layers of neurons to capture complex nonlinear relationships between lagged values and future predictions.

Key Concept: Unlike linear models, DNNs can learn nonlinear transformations through activation functions (ReLU, tanh). Each hidden layer learns increasingly abstract features. The model is trained using backpropagation with the Adam optimizer to minimize Mean Squared Error (MSE).

Benchmark: We compare DNN performance against Linear Regression to understand when the added complexity provides value.

Model Configuration

Number of Lags (Input Features)

Forecast Horizon (months ahead)

Number of Hidden Layers

Hidden Layer Activation

Output Layer Activation

Training Epochs

200

Neurons per Layer

When Do DNNs Outperform Linear Models for Time Series?

Nonlinear Patterns: DNNs excel when relationships between lags and future values are nonlinear (e.g., exponential growth, complex seasonality)
Feature Interactions: Hidden layers can learn interactions between different lag features automatically
Sufficient Data: DNNs need more training data than linear models to avoid overfitting
Overfitting Risk: Too many layers/neurons can lead to memorizing training data rather than learning generalizable patterns
Activation Functions: ReLU enables learning of piecewise linear functions; Tanh provides smooth nonlinearities; Linear activation means no transformation (useful for comparison)
Training Duration: More epochs allow the model to learn better but may lead to overfitting if too many
Interpretability Trade-off: While DNNs may provide better predictions, they are less interpretable than linear models or traditional time series methods like ARIMA

Deep Neural Networks for Time Series Forecasting

Understanding Deep Neural Networks for Time Series

Model Configuration

Neurons per Layer

Training Set Performance

Test Set Performance

When Do DNNs Outperform Linear Models for Time Series?