combine wavelet transform and attention mechanism for time series forecasting or classification
This code is implementation of "Forecasting Wavelet Transformed Time Series with Attentive Neural Networks" (ICDM 2018).
The models are defined in the core directory.
The experiments on two datasets are defined in power and stock directories, respectively.
The hyper-parameters could be set as arguments to the scripts like this:
python stock_data.py --ahead_step=1 --time_window=5 --num_frequencies=5 --lstm_units=8 --max_training_iters=50 --keep_prob=1.0 --model_structure=1 --notes=pure_lstm --learning_rate=0.01
where model_structure determines which model you choose:
- model_structure = 1: LSTM;
- model_structure = 2: CNN;
- model_structure = 3: Our attentive neural network;
- model_structure = 4: ensemble of LSTM and CNN.
The data could be downloaded according to the website provided in the paper.