LSTMForecaster


LSTMForecaster

Introduction

Long short-term memory(LSTM) is a special type of recurrent neural network(RNN). We implement the basic version of LSTM - VanillaLSTM for this forecaster for time-series forecasting task. It has two LSTM layers, two dropout layer and a dense layer. LSTMForecaster is derived from tfpark.KerasMode, and can use all methods of KerasModel. Refer to tfpark.KerasModel API Doc for details.

For the detailed algorithm description, please refer to here.

Method

Arguments

__init__

LSTMForecaster(target_dim=1,
               feature_dim=1,
               lstm_1_units=16,
               dropout_1=0.2,
               lstm_2_units=8,
               dropout_2=0.2,
               metric="mean_squared_error",
               lr=0.001,
               loss="mse",
               uncertainty: bool = False
               )

fit, evaluate, predict

Refer to fit, evaluate, predict defined in tfpark.KerasModel API Doc