MSE: 0.2088MAE: 0.371
Papers with Code · CC BY-SA 4.0
Time Series Forecasting
Time Series Forecasting is the task of fitting a model to historical, time-stamped data in order to predict future values. Traditional approaches include moving average, exponential smoothing, and ARIMA, though models as various as RNNs, Transformers, or XGBoost can also be applied. The most popular benchmark is the ETTh1 dataset. Models are typically evaluated using the Mean Square Error (MSE) or Root Mean Square Error (RMSE).
<span style="color:grey; opacity: 0.6">( Image credit: ThaiBinh Nguyen )</span>
논문
MSE: 0.166MAE: 0.329
MSE: 0.166MAE: 0.323
MSE: 0.194MAE: 0.355
MSE: 0.391
MSE: 0.171MAE: 0.336
MSE: 0.263MAE: 0.239
MSE: 0.204MAE: 0.367
MSE: 0.166MAE: 0.332
MSE: 0.18MAE: 0.345
Traffic transformer: Capturing the continuity and periodicity of time series for traffic forecasting
RMSE: 0.072
MSE: 1.226MAE: 0.653