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Seq2Seq with LSTM

The sequence to sequence model originates from language translation. Our implementation adapts the model for multi-step time series forecasting. Specifically, given the input series x1,…,xtx_1, \ldots, x_{t}, the model maps the input series to the output series:

xt−p,xt−p+1,…,xt−1⟶xt,xt+1,…,xt+h−1x_{t-p}, x_{t-p+1}, \ldots, x_{t-1} \longrightarrow x_t, x_{t+1}, \ldots, x_{t+h-1}

where pp is the input history length and hh is the forecasting horizon.

Sequence to sequence (Seq2Seq) models consist of an encoder and a decoder. The final state of the encoder is fed as the initial state of the decoder. We can use various models tor both the encoder and decoder. This function implements a Long Short Term Memory (LSTM).