## Can seasonal data be stationary?

Thus, time series with trends, or with seasonality, are not stationary — the trend and seasonality will affect the value of the time series at different times. On the other hand, a white noise series is stationary — it does not matter when you observe it, it should look much the same at any point in time.

## Why is AR 1 stationary?

The AR(1) process is stationary if only if |φ| < 1 or −1 <φ< 1. This is a non-stationary explosive process. If we combine all the inequalities we obtain a region bounded by the lines φ2 =1+ φ1; φ2 = 1 − φ1; φ2 = −1. For the stationarity condition of the MA(q) process, we need to rely on the general linear process.

**What if time series is not stationary?**

Non-stationary data, as a rule, are unpredictable and cannot be modeled or forecasted. The results obtained by using non-stationary time series may be spurious in that they may indicate a relationship between two variables where one does not exist.

**Are autoregressive models stationary?**

Contrary to the moving-average (MA) model, the autoregressive model is not always stationary as it may contain a unit root.

### Is Arma always stationary?

An ARMA model is a stationary model; If your model isn’t stationary, then you can achieve stationarity by taking a series of differences. A model with a dth difference to fit and ARMA(p,q) model is called an ARIMA process of order (p,d,q).

### How do you know if a variable is stationary?

Probably the simplest way to check for stationarity is to split your total timeseries into 2, 4, or 10 (say N) sections (the more the better), and compute the mean and variance within each section. If there is an obvious trend in either the mean or variance over the N sections, then your series is not stationary.

**Is the correlogram model suitable for stationary series?**

Correlogram has very few significant spikes at very small u000blags and cuts off drastically/dies down quickly for stationary series. Thus model 2 produces stationary series, where as model 1 does not. Also, model 2 is suitable for further time series analysis.

**Which is the best description of autocorrelation?**

Autocorrelation Function. Correlation of a time series with its own past and future values- is called Autocorrelation. It is also referred as “lagged or series correlation”. Positive autocorrelation is an indication of a specific form of “persistence”, the tendency of a system to remain in the same state from one observation to the next…

#### What is the deﬁnition of weak stationarity?

With autocovariance functions, we can deﬁne the covariance stationarity, or weak stationarity. Inthe literature, usually stationarity means weak stationarity, unless otherwise speciﬁed. Deﬁnition 2(Stationarity or weak stationarity) The time series{Xt, t∈Z}(where Zis theinteger set) is said to be stationary if(I)E(X2