ScholarGate
Assistent

Sammenlign metoder

Gennemgå dine valgte metoder side om side; rækker, der afviger, er fremhævet.

Autoregressiv model (AR)×Moving Average (MA) Model×SARIMA-model×
FagområdeØkonometriØkonometriØkonometri
FamilieRegression modelRegression modelRegression model
Oprindelsesår1970s (popularised 1976)19701970 (first edition); 1976 (revised)
OphavspersonGeorge E. P. Box and Gwilym M. JenkinsBox and JenkinsBox, Jenkins, and Reinsel
TypeTime series modelLinear time series modelSeasonal time series model
Oprindelig kildeBox, G. E. P., & Jenkins, G. M. (1976). Time Series Analysis: Forecasting and Control (revised ed.). Holden-Day. ISBN: 978-0816211043Box, G. E. P., Jenkins, G. M., & Reinsel, G. C. (1976). Time Series Analysis: Forecasting and Control (revised ed.). Holden-Day. ISBN: 978-0130607744Box, G. E. P., Jenkins, G. M., & Reinsel, G. C. (1976). Time Series Analysis: Forecasting and Control (revised ed.). Holden-Day. ISBN: 978-0130607744
AliasserAR model, AR(p) model, autoregression, AR processMA model, MA(q) process, moving-average process, Box-Jenkins MASARIMA, seasonal ARIMA, Box-Jenkins seasonal model, ARIMA with seasonal component
Relaterede655
ResuméAn autoregressive model of order p — AR(p) — expresses the current value of a time series as a linear function of its own p most recent past values plus a white-noise error. It is the building block of the Box-Jenkins family of time-series models and is widely used for forecasting stationary economic and financial series.The Moving Average model of order q — written MA(q) — expresses the current value of a time series as a linear combination of the current and past random shocks (innovations). Unlike the AR model which uses lagged values of the series itself, the MA model uses lagged error terms, making it well-suited for capturing short-lived disturbances that dissipate over q periods.SARIMA extends ARIMA by adding seasonal autoregressive and moving-average operators to capture repeating patterns at fixed intervals — such as monthly, quarterly, or annual cycles. Denoted SARIMA(p,d,q)(P,D,Q)s, it is the standard workhorse for univariate seasonal time series forecasting in econometrics, economics, and official statistics.
ScholarGateDatasæt
  1. v1
  2. 2 Kilder
  3. PUBLISHED
  1. v1
  2. 2 Kilder
  3. PUBLISHED
  1. v1
  2. 2 Kilder
  3. PUBLISHED

Gå til søgning Hent slides

ScholarGateSammenlign metoder: Autoregressive model · Moving Average Model · SARIMA model. Hentet 2026-06-18 fra https://scholargate.app/da/compare