Temporal Convolutional Network (TCN)

A temporal convolutional network (TCN) is a deep learning architecture for sequence data that uses causal, dilated one-dimensional convolutions with residual connections to model long-range temporal dependencies.

시간 합성곱 신경망(TCN)은 인과적 팽창(dilated) 1차원 합성곱과 잔차 연결을 사용해 장기 시간 의존성을 모형화하는 시계열 딥러닝 구조이다.

How do I forecast a time series with a deep learning model that captures long-range patterns?

장기 패턴을 포착하는 딥러닝 모형으로 시계열을 어떻게 예측하나요?

A temporal convolutional network stacks causal convolutions with increasing dilation so each prediction uses only past values while its receptive field covers long histories. It fits long univariate or multivariate series, or many related series, where nonlinear patterns and long lags matter and enough data are available to train a neural network.

When to use Temporal Convolutional Network (TCN)

• Long time series or many related series with nonlinear patterns
• Sequence-to-value or multi-step forecasting with long input windows
• Sensor, energy, or demand data with complex temporal dependencies

When to use something else

• The series is short or has simple trend and seasonality: use ARIMA or exponential smoothing
• Interpretable decomposed components are required: use Prophet or NeuralProphet

Assumptions and requirements

• Sufficient training data relative to network size
• Scaled inputs and a fixed input window length
• Time-ordered train, validation, and test splits without leakage
• Hyperparameter tuning of kernel size, dilations, and dropout

What the analysis reports

• Forecasts for the test period
• Error metrics such as MAE, RMSE, or MAPE on a holdout set
• Training and validation loss curves
• Comparison against a naive or statistical baseline

How to report it

The TCN achieved a test-set RMSE of [value] for [horizon]-step-ahead forecasts, compared with [value] for the [baseline] baseline.

Related methods

• LSTM Network
• ARIMA Model
• 1D Convolutional Neural Network

Run Temporal Convolutional Network (TCN) on Quorant