attentioned-dual-stage-stock-prediction

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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, —test - test phase

-m, —model - 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