Compute을(를) 사용하는 경우, 시스템은 각 예측변수에 대한 예측 기간의 미래 값을 계산합니다.
각 예측변수에 대해 함수 목록에서 선택할 수 있습니다(공백, 최근 지점의 평균, 가장 최근 값). 또는 specify을(를) 사용하여 수동으로 값을 입력할 수 있습니다. 개별 필드 및 특성을 지정하려면 extend_metric_values 특성을 사용하십시오. 예를 들어,
set :ts.futureValue_type_method="specify"
set :ts.extend_metric_values=[{'Market_1','USER_SPECIFY', [1,2,3]},
{'Market_2','MOST_RECENT_VALUE', ''},{'Market_3','RECENT_POINTS_MEAN', ''}]
exsmooth_transformation_type
None SquareRoot NaturalLog
arima.p
정수
arima.d
정수
arima.q
정수
arima.sp
정수
arima.sd
정수
arima.sq
정수
arima_transformation_type
None SquareRoot NaturalLog
arima_include_constant
플래그
tf_arima.p.필드 이름
정수
전이 함수용입니다.
tf_arima.d.필드 이름
정수
전이 함수용입니다.
tf_arima.q.필드 이름
정수
전이 함수용입니다.
tf_arima.sp.필드 이름
정수
전이 함수용입니다.
tf_arima.sd.필드 이름
정수
전이 함수용입니다.
tf_arima.sq.필드 이름
정수
전이 함수용입니다.
tf_arima.delay.필드 이름
정수
전이 함수용입니다.
tf_arima.transformation_type.필드 이름
None SquareRoot NaturalLog
전이 함수용입니다.
arima_detect_outliers
플래그
arima_outlier_additive
플래그
arima_outlier_level_shift
플래그
arima_outlier_innovational
플래그
arima_outlier_transient
플래그
arima_outlier_seasonal_additive
플래그
arima_outlier_local_trend
플래그
arima_outlier_additive_patch
플래그
max_lags
정수
cal_PI
플래그
conf_limit_pct
실수
events
필드
continue
플래그
scoring_model_only
플래그
매우 많은
시계열 수(수만 개)가 있는 모델에 사용합니다.
forecastperiods
정수
extend_records_into_future
플래그
extend_metric_values
필드
예측자의 미래 값을 제공할 수 있습니다.
conf_limits
플래그
noise_res
플래그
max_models_output
정수
출력에 표시될 모델 수를 제어합니다. 기본값은 10입니다. 작성된
총 모델 수가 이 값을 초과할 경우 모델이 출력에 표시되지 않습니다. 여전히
모델을 스코어링에 사용할 수 있습니다.
missing_value_threshold
이중 실선
각 시계열에 대한 시간 변수 및 입력 데이터에 대한 데이터 품질 측도를 계산합니다. 데이터 품질 점수가 이 임계값보다 낮으면 해당 시계열이 버려집니다.
compute_future_values_input
부울
False: 입력의 미래 값을 계산합니다. True: 데이터에 추가할 값이 있는 필드를 선택하십시오.
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> Governance > Categories
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시계열 노드는 시계열 데이터에 대한 지수평활, 일변량
자기회귀 통합 이동 평균(ARIMA), 다변량 ARIMA(또는 전이 함수) 모델을 추정하고
미래 성능을 위한 예측값을 생성합니다.