time-series-13
Time Series Forecasting
3260 papers • 126 benchmarks • 313 datasets
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). ( Image credit: ThaiBinh Nguyen )
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Benchmarks
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ETTh1 (336) Multivariate
ETTh1 (336) Multivariate
ETTh1 (720) Multivariate
ETTh1 (720) Multivariate
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17 papers 785
