Codebase for "Synergetic Learning of Heterogeneous Temporal Sequences for Multi-Horizon Probabilistic Forecasting"
- data_process.py
- Transform raw time-series data to preprocessed time-series data
- Extract events from variables
- Create formatted data for training
- dataset.py
- Dataset class for heterogeneous sequences
- evaluation.py
- Functions for evaluation of forecasting resutls
- VSMHN.py
- Implementation codes of the proposed model
- run.py
- Runner
- utils.py
- some useful utility functions
properscoring==0.1
scipy==1.2.1
torch==1.3.0
numpy==1.18.1
requests==2.21.0
tqdm==4.36.1
pandas==0.25.3
matplotlib==3.0.3
scikit_learn==0.23.2
pip install -r requirements.txt
usage: run.py [-h] [--X_context X_CONTEXT] [--y_horizon Y_HORIZON]
[--window_skip WINDOW_SKIP] [--train_prop TRAIN_PROP]
[--h_dim H_DIM] [--z_dim Z_DIM] [--use_GRU USE_GRU] [--lr LR]
[--dec_bound DEC_BOUND] [--batch_size BATCH_SIZE]
[--epochs EPOCHS] [--device DEVICE] [--mc_times MC_TIMES]
optional arguments:
-h, --help show this help message and exit
--X_context X_CONTEXT
observing time length (default: 168)
--y_horizon Y_HORIZON
predicting time length (default: 24)
--window_skip WINDOW_SKIP
skipping step for data generation (default: 12)
--train_prop TRAIN_PROP
percent of data used for trainning (default: 0.97)
--h_dim H_DIM dimension for ts/event encoder and decoder (default:
200)
--z_dim Z_DIM dimension for latent variable encoder (default: 100)
--use_GRU USE_GRU RNN cell type(True:GRU, False:LSTM) (default: True)
--lr LR learning_rate (default: 0.001)
--dec_bound DEC_BOUND
dec_bound for std (default: 0.05)
--batch_size BATCH_SIZE
batch size (default: 400)
--epochs EPOCHS trainning epochs (default: 100)
--device DEVICE select device (cuda:0, cpu) (default: cuda:0)
--mc_times MC_TIMES num of monte carlo simulations (default: 1000)
$ python run.py --h_dim 200 --z_dim 100 --batch_size 400 --epochs 100 --device cuda:0
- CRPS score and RMSE score of forecast variables
- forecast_plots.png visualizes forecast results