DA-LSTM
This is an implementation of paper "A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction". I only did a test to predict the price of AAPL.US by its historical data as well as the price of its opponent MSFT.US.
Dataset
Downloaded from NASDAQ 100 STOCK DATA.
Argument
-e, --epoch - the number of epochs
-b, --batch - the batch size
-s, --split - the split ratio of train and test set
-i, --interval - save models every interval epochs
-l, --lrate - learning rate of optimizor
-t, โtest - test phase
-m, โmodel - if in test phase, the models name(if model name is "encoder50" and decoder50", inptut 50)
Sample train
Traing 500 epochs, with batch-size 128, save models every 100 epochs.
Python3 trainer -e 500 -b 128 -i 100
Sample test
Test data use model "encoder50" and "decoder50"
Python3 trainer -t -m 50