Source codes for the paper "probabilistic forecasting with temporal convolutional neural network"
- Download the dataset from UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/datasets/ElectricityLoadDiagrams20112014
- Run "R CMD BATCH electricity/basicPreprocess.R" to generate "modelData.csv" for model training.
- python3 electricity/ecPointModel/ec_feature_preprocess.py
- python3 electricity/ecPointModel/ECPointHuber.py
- python3 electricity/NewTCNQuantile/ec_feature_preprocess.py
- python3 electricity/NewTCNQuantile/ec_probabilistic_forecasting.py
- Download the dataset from UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/datasets/PEMS-SF
- Run "R CMD BATCH traffic/basicPreprocess.R" to generate "traffic.csv".
- python3 traffic/traffic_feature_preprocess.py to generate the "tensor_prepare.pkl" for the model training
- python3 traffic/point/traffic_point_forecasting.py
- python3 traffic/quantile/traffic_quantile_forecasting.py
Probabilistic forecasting with temporal convolutional neural network
KDD 2019 ,Workshop on Mining and Learning from Time Series, 2019
The total project will be refined in the next months. Also, you can achieve better results if you do better data preprocessing like scaling.