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Time Series Forecasting

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋
하위 태스크
6
평가표
86
보존된 평가 기록
538

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>

평가 결과

데이터셋과 평가 조건을 선택해 성능·논문·코드를 비교하세요. 보존된 Papers with Code 기록으로, 최신 순위는 아닙니다.

Weather (192)

원본 태스크: Time Series Analysis / Time Series Forecasting

평가 기록 13건

Electricity

원본 태스크: Time Series Analysis / Time Series Forecasting / Univariate Time Series Forecasting

평가 기록 12건

Weather (96)

원본 태스크: Time Series Analysis / Time Series Forecasting

평가 기록 12건

Electricity (96)

원본 태스크: Time Series Analysis / Time Series Forecasting

평가 기록 11건

Weather (336)

원본 태스크: Time Series Analysis / Time Series Forecasting

평가 기록 11건

Weather (720)

원본 태스크: Time Series Analysis / Time Series Forecasting

평가 기록 11건

Electricity (336)

원본 태스크: Time Series Analysis / Time Series Forecasting

평가 기록 9건

Electricity (192)

원본 태스크: Time Series Analysis / Time Series Forecasting

평가 기록 8건

Electricity (720)

원본 태스크: Time Series Analysis / Time Series Forecasting

평가 기록 8건

USHCN-Daily

원본 태스크: Time Series Analysis / Time Series Forecasting / Multivariate Time Series Forecasting

평가 기록 8건

MLO-Cn2

원본 태스크: Time Series Analysis / Time Series Forecasting

평가 기록 7건

PeMSD7

원본 태스크: Time Series Analysis / Time Series Forecasting

평가 기록 7건

ETTh1 (96) Univariate

원본 태스크: Time Series Analysis / Time Series Forecasting

평가 기록 6건

ETTh2 (96) Univariate

원본 태스크: Time Series Analysis / Time Series Forecasting

평가 기록 6건

MuJoCo

원본 태스크: Time Series Analysis / Time Series Forecasting / Multivariate Time Series Forecasting

평가 기록 6건

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.