# SARIMA

The SARIMA model is an extension of the ARIMA model, often used when we suspect a model may have a seasonal effect.

By definition, the seasonal auto-regressive integrated moving average - SARIMA(p,d,q)(P,D,Q)s - process is a multiplicative of two ARMA processes of the differenced time series.

$(1-\sum_{i=1}^p {\phi_i L^i})(1-\sum_{j=1}^P {\Phi_j L^{j \times s}})(1-L)^d (1-L^s)^D x_t = (1+\sum_{i=1}^q {\theta_i L^i})(1+\sum_{j=1}^Q {\Theta_j L^{j \times s}}) a_t$ $y_t = (1-L)^d (1-L^s)^D x_t$

Where:

• $$x_t$$ is the original non-stationary output at time t.
• $$y_t$$ is the differenced (stationary) output at time t.
• $$d$$ is the non-seasonal integration order of the time series.
• $$p$$ is the order of the non-seasonal AR component.
• $$P$$ is the order of the seasonal AR component.
• $$q$$ is the order of the non-seasonal MA component.
• $$Q$$ is the order of the seasonal MA component.
• $$s$$ is the seasonal length.
• $$D$$ is the seasonal integration order of the time series.
• $$a_t$$ is the innovation, shock or the error term at time t.
• $$\{a_t\}$$ time series observations are independent and identically distributed (i.e. i.i.d) and follow a Gaussian distribution (i.e. $$\Phi(0,\sigma^2)$$)

Assuming y_t follows a stationary process with a long-run mean of \mu, then taking the expectation from both sides, we can express \phi_o as follows:

$\phi_o = (1-\phi_1-\phi_2-\cdots-\phi_p)(1-\Phi_1-\Phi_2-\cdots-\Phi_P)$

Thus, the SARIMA(p,d,q)(P,D,Q)s process can now be expressed as:

$(1-\sum_{i=1}^p {\phi_i L^i})(1-\sum_{j=1}^P {\Phi_j L^{j \times s}}) (y_t -\mu) = (1+\sum_{i=1}^q {\theta_i L^i})(1+\sum_{j=1}^Q {\Theta_j L^{j \times s}}) a_t$ $z_t=y_t-\mu$ $(1-\sum_{i=1}^p {\phi_i L^i})(1-\sum_{j=1}^P {\Phi_j L^{j \times s}}) z_t = (1+\sum_{i=1}^q {\theta_i L^i})(1+\sum_{j=1}^Q {\Theta_j L^{j \times s}}) a_t$

In sum, $$z_t$$ is the differenced signal after we subtract its long-run average.

Notes: The order of the seasonal or non-seasonal AR (or MA) component is solely determined by the order of the last lagged variable with a non-zero coefficient. In principle, you can have fewer parameters than the order of the component.

Remarks
1. The variance of the shocks is constant or time-invariant.
2. The order of the seasonal or non-seasonal AR (or MA) component is solely determined by the order of the last lagged variable with a non-zero coefficient. In principle, you can have fewer parameters than the order of the component.
3. Example: Consider the following SARIMA(0,1,1)(0,1,1)12 process: $(1-L)(1-L^{12})x_t-\mu = (1+\theta L)(1+\Theta L^{12})a_t$ Note: This is the AIRLINE model, a special case of the SARIMA model.
Requirements
Header SFSDK.H SFSDK.LIB SFSDK.DLL
References
* Hamilton, J .D.; Time Series Analysis , Princeton University Press (1994), ISBN 0-691-04289-6
* Tsay, Ruey S.; Analysis of Financial Time Series John Wiley & SONS. (2005), ISBN 0-471-690740
* D. S.G. Pollock; Handbook of Time Series Analysis, Signal Processing, and Dynamics; Academic Press; Har/Cdr edition(Nov 17, 1999), ISBN: 125609906
* Box, Jenkins and Reisel; Time Series Analysis: Forecasting and Control; John Wiley & SONS.; 4th edition(Jun 30, 2008), ISBN: 470272848