ZIP基于RNN、GRU、LSTM和Attention的“时间序列预测”代码 22.62KB

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资源文件列表:

Time-series-prediction-master.zip 大约有9个文件
  1. Time-series-prediction-master/
  2. Time-series-prediction-master/README.md 1.19KB
  3. Time-series-prediction-master/basic_attention.py 7.92KB
  4. Time-series-prediction-master/basic_rnn_lstm_gru.py 4.06KB
  5. Time-series-prediction-master/data/
  6. Time-series-prediction-master/data/google.csv 35.4KB
  7. Time-series-prediction-master/data_loader.py 1.57KB
  8. Time-series-prediction-master/main_time_series_prediction.py 3.16KB
  9. Time-series-prediction-master/utils.py 2.71KB

资源介绍:

包含使用RNN、GRU、LSTM或Attention方法进行基本时间序列预测的实现。在时间序列预测任务中，RNN、GRU、LSTM和Attention是常用的深度学习模型。通过使用这些模型，可以捕获时间序列数据中的长期依赖关系和模式，从而有效地预测未来时间点的数值或趋势。 在实现方面，可使用深度学习框架如TensorFlow或PyTorch来构建模型。这些框架提供了各种RNN、GRU、LSTM和Attention层的实现，使模型构建和训练过程更加简单。
# 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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