How to make LSTM predict multiple time steps ahead?

Question

I am trying to use a LSTM for time series prediction. The data streams in once per minute, but I would like to predict an hour ahead. There are two ways I can think of for going about this:

1. Squash the data into hourly data instead, taking the average over each 60 minute time period as one data point.
2. For each (X, y) training data pair, let X be the time series from t - 120 to t - 60, and let y be the time series from t - 60 to t. Force the LSTM to predict 60 timesteps ahead, and take y[-1] as the prediction.

Are there any best practices for going about this?

Answer 1

There are different approaches

• Recursive strategy

  • one many-to-one model

    prediction(t+1) = model(obs(t-1), obs(t-2), ..., obs(t-n))
    prediction(t+2) = model(prediction(t+1), obs(t-1), ..., obs(t-n)) 
    

• Direct strategy

  • multiple many-to-one models

    prediction(t+1) = model1(obs(t-1), obs(t-2), ..., obs(t-n))
    prediction(t+2) = model2(obs(t-2), obs(t-3), ..., obs(t-n))`
    

• Multiple output strategy

  • one many-to-many model

    prediction(t+1), prediction(t+2) = model(obs(t-1), obs(t-2), ..., obs(t-n))`
    

• Hybrid Strategies

  • combine two or more above strategies

Reference : Multi-Step Time Series Forecasting

Answer 2

From https://machinelearningmastery.com/multi-step-time-series-forecasting-long-short-term-memory-networks-python/

train = [[t-120,t-199...t,t+1...t+60],[...],[...]]

# fit an LSTM network to training data
def fit_lstm(train, n_lag, n_seq, n_batch, nb_epoch, n_neurons):
    # reshape training into [samples, timesteps, features]
    X, y = train[:, 0:n_lag], train[:, n_lag:]
    X = X.reshape(X.shape[0], 1, X.shape[1])
    # design network
    model = Sequential()
    model.add(LSTM(n_neurons, batch_input_shape=(n_batch, X.shape[1], X.shape[2]), stateful=True))
    model.add(Dense(y.shape[1]))
    model.compile(loss='mean_squared_error', optimizer='adam')
    # fit network
    for i in range(nb_epoch):
        model.fit(X, y, epochs=1, batch_size=n_batch, verbose=0, shuffle=False)
        model.reset_states()
    return model