文件列表:

basic_attention.py (8115, 2020-03-27)
basic_rnn_lstm_gru.py (4157, 2020-03-27)
data (0, 2020-03-27)
data\google.csv (36251, 2020-03-27)
data_loader.py (1607, 2020-03-27)
main_time_series_prediction.py (3237, 2020-03-27)
utils.py (2771, 2020-03-27)

# Codebase for "Time-series prediction" with RNN, GRU, LSTM and Attention Authors: Jinsung Yoon Contact: jsyoon0823@gmail.com This directory contains implementations of basic time-series prediction using RNN, GRU, LSTM or Attention methods. To run the pipeline, simply run python3 -m main_time_series_prediction.py. ## Stages of time-series prediction framework: - Load dataset (Google stocks data) - Train model: (1) RNN based: Simple RNN, GRU, LSTM (2) Attention based - Evaluate the performance: MAE or MSE metrics ### Command inputs: - train_rate: training data ratio - seq_len: sequence length - task: classification or regression - model_type: rnn, lstm, gru, or attention - h_dim: hidden state dimensions - n_layer: number of layers - batch_size: the number of samples in each mini-batch - epoch: the number of iterations - learning_rate: learning rates - metric_name: mse or mae ### Example command ```shell $ python3 main_time_series_prediction.py --train_rate 0.8 --seq_len 7 --task regression --model_type lstm --h_dim 10 --n_layer 3 --batch_size 32 --epoch 100 --learning_rate 0.01 --metric_name mae ``` ### Outputs - MAE or MSE performance of trained model

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