Bandingkan metode
Tinjau metode pilihan Anda berdampingan; baris yang berbeda akan disorot.
| Model ARIMA (Autoregressive Integrated Moving Average)× | Model Peralihan Rezim Markov (MS-AR / MS-VAR)× | Model Deret Waktu Struktural (Model Struktural Dasar)× | |
|---|---|---|---|
| Bidang | Ekonometrika | Ekonometrika | Ekonometrika |
| Keluarga | Regression model | Regression model | Regression model |
| Tahun asal≠ | 2015 | 1989 | 1990 |
| Pencetus≠ | Box & Jenkins (Box-Jenkins methodology) | Hamilton (1989); Kim & Nelson (1999) | Andrew C. Harvey |
| Tipe≠ | Univariate time-series model | Regime-switching time series model | State-space (unobserved components) time series model |
| Sumber perintis≠ | Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021 | Hamilton, J. D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica, 57(2), 357-384. DOI ↗ | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. ISBN: 978-0521405737 |
| Alias≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | regime-switching model, Markov-switching autoregression, MS-AR, MS-VAR | BSM, basic structural model, unobserved components model, Yapısal Zaman Serisi Modeli (BSM) |
| Terkait≠ | 5 | 5 | 4 |
| Ringkasan≠ | ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015). | The Markov regime-switching model lets the parameters of a time series change probabilistically across hidden regimes governed by a Markov chain. Introduced by Hamilton (1989) and developed further by Kim and Nelson (1999), it automatically detects business-cycle phases such as expansions and contractions. | The Structural Time Series Model, in its Basic Structural Model (BSM) form, is Andrew Harvey's state-space approach that decomposes a series into separate stochastic trend, seasonal, cyclical, and irregular components. Developed in Harvey's 1990 treatment, it is prized for interpretability and component decomposition where ARIMA only delivers a black-box fit. |
| ScholarGateSet data ↗ |
|
|
|