Recurrent Neural Network (LSTM, GRU, BiLSTM)

Recurrent neural networks such as LSTM, GRU, and bidirectional LSTM are deep learning models that process sequences step by step with a hidden state, using gating mechanisms to learn long-range dependencies.

LSTM, GRU, 양방향 LSTM과 같은 순환신경망은 은닉상태를 이용해 시퀀스를 한 단계씩 처리하고 게이트 구조로 장기 의존성을 학습하는 딥러닝 모형입니다.

How do I use deep learning to model or forecast sequential data?

딥러닝으로 순차 데이터를 모형화하거나 예측하려면 어떻게 하나요?

Recurrent neural networks, especially LSTM and GRU variants, learn patterns in sequential data by carrying a hidden state forward through time, with gates that help them capture long-range dependencies. They fit long series or many sequences with nonlinear dynamics, while bidirectional LSTMs read the sequence in both directions and are suited to sequence labelling rather than forecasting the future.

When to use Recurrent Neural Network (LSTM, GRU, BiLSTM)

• Long time series with nonlinear patterns and enough data to train a neural network
• Sequence classification or labelling, such as sensor or text sequences
• Multivariate sequences where several inputs evolve together

When to use something else

• Short univariate series: use ARIMA or exponential smoothing
• Forecasting future values: avoid bidirectional LSTM, because it uses future context within the input window

Assumptions and requirements

• Substantial sequence data for training
• Scaled inputs and a fixed input window length
• Chronological train-validation-test split to avoid leakage
• Regularization or early stopping to limit overfitting

What the analysis reports

• Training and validation loss curves
• Forecasts or predicted labels versus actual values
• Test-set RMSE and MAE, or accuracy and F1 for classification
• Comparison with a statistical baseline

How to report it

The LSTM model achieved a test RMSE of [value] and MAE of [value], compared with [value] and [value] for the ARIMA baseline.

Related methods

• Temporal Fusion Transformer (TFT)
• DeepAR Forecasting
• ARIMA

Run Recurrent Neural Network (LSTM, GRU, BiLSTM) on Quorant