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AngelWings1997 215b16c07c | 1 year ago | |
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This repository provides Univariate Time Series Prediction. It supports;
The dataset used is Appliances Energy Prediction Data Set and can be found here.
According to the table below, CNN using 1D Convolutional layer outperformed the other models on single-step time series prediction.
Model | MAE↓ | MSE↓ | RMSE↓ | MPE↓ | MAPE↓ | R Squared↑ |
---|---|---|---|---|---|---|
DNN | 29.3038 | 3673.7921 | 57.0114 | -15.6800 | 26.4763 | 0.3820 |
CNN | 27.5182 | 3614.1634 | 56.1604 | -11.2039 | 23.7301 | 0.4057 |
RNN | 29.1327 | 3627.1491 | 56.7243 | -16.2193 | 26.9323 | 0.3809 |
LSTM | 29.6157 | 3575.5541 | 56.4002 | -16.7178 | 27.9683 | 0.3771 |
GRU | 29.0402 | 3564.9701 | 56.2790 | -16.9984 | 26.9390 | 0.3872 |
Attentional LSTM | 28.9658 | 3603.0751 | 56.3838 | -16.8199 | 26.3129 | 0.3898 |
According to the table below, DNN outperformed the other models on multi-step time series prediction.
Model | MAE↓ | MSE↓ | RMSE↓ | MPE↓ | MAPE↓ | R Squared↑ |
---|---|---|---|---|---|---|
DNN | 31.3555 | 2913.6521 | 49.3946 | -16.7329 | 29.1459 | 0.1775 |
CNN | 32.9762 | 2893.2201 | 49.5900 | -21.7513 | 32.3016 | 0.1206 |
RNN | 32.9153 | 2951.9055 | 50.0931 | -20.7460 | 32.2081 | 0.1223 |
LSTM | 32.8141 | 2955.5278 | 50.1237 | -20.5471 | 32.0873 | 0.1191 |
GRU | 33.0092 | 2927.5575 | 49.9503 | -21.2869 | 32.5345 | 0.1177 |
Attentional LSTM | 32.2182 | 2920.8744 | 49.7972 | -19.1188 | 30.8223 | 0.1347 |
It definitely suffers from the typical lagging issue. Also, I averaged multi-step for plotting thus it looks to be smoothed.
If you want to train Attention LSTM,
python main.py --model 'attention'
If you want to train with multi-step with time step of 5,
python main.py --model 'attention' --multi_step True --output_size 5
python main.py --model 'attention' --mode 'test'
To handle more arguments, you can refer to here.
- Windows 10 Home
- NVIDIA GFORCE RTX 2060
- CUDA 10.2
- torch 1.6.0
- torchvision 0.7.0
- etc
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