ScholarGate
Pembantu

Bandingkan kaedah

Semak kaedah pilihan anda secara bersebelahan; baris yang berbeza akan diserlahkan.

Model Purata Bergerak (MA)×Model ARIMA (Autoregressive Integrated Moving Average)×Model ARMA (Autoregresif Moving Average)×
BidangEkonometrikEkonometrikEkonometrik
KeluargaRegression modelRegression modelRegression model
Tahun asal197019701970
PengasasBox and JenkinsGeorge Box and Gwilym JenkinsGeorge E. P. Box and Gwilym M. Jenkins
JenisLinear time series modelTime series forecasting modelTime series model
Sumber perintisBox, 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. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗
AliasMA model, MA(q) process, moving-average process, Box-Jenkins MAARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)ARMA, Box-Jenkins model, autoregressive moving average, AR(p)MA(q)
Berkaitan565
RingkasanThe 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.The ARIMA(p,d,q) model is the standard workhorse for univariate time series forecasting. It combines autoregressive terms (past values), differencing to induce stationarity, and moving average terms (past shocks) into a unified linear framework. Developed by Box and Jenkins (1970), it remains one of the most widely applied models in econometrics and applied statistics.The ARMA(p,q) model describes a stationary time series as a combination of two components: an autoregressive part that regresses the current value on its own past p values, and a moving average part that accounts for past q error terms. It is the foundational framework of the Box-Jenkins methodology for univariate time series modelling and short-run forecasting.
ScholarGateSet data
  1. v1
  2. 2 Sumber
  3. PUBLISHED
  1. v1
  2. 2 Sumber
  3. PUBLISHED
  1. v1
  2. 2 Sumber
  3. PUBLISHED

Pergi ke carian Muat turun slaid

ScholarGateBandingkan kaedah: Moving Average Model · ARIMA model · ARMA model. Dicapai 2026-06-18 daripada https://scholargate.app/ms/compare