LSTM Implementation (Code Example)

Using high-level libraries (Keras, PyTorch) simplifies LSTM usage.

define and train a Keras LSTM on a univariate time series:

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense

# X_train shape: (samples, timesteps, 1)
model = Sequential([
    LSTM(32, input_shape=(None, 1)),
    Dense(1)
])
model.compile(optimizer='adam', loss='mse')
model.fit(X_train, y_train, epochs=20, batch_size=16)

The model learns to map sequences to outputs; input sequences can be constructed via sliding windows.