Time series data with seasonality using VAR? I have two time series:
1) Which only contains historical data for production 2006-2011 on a monthly basis.
2) Which contains both historical and projected flow data 2006-2057 on a monthly basis.
I would like to use VAR to use the flow data as a predictor for the production. My problem is that the data is seasonal and I don't know how to handle VAR with seasonality? If I use SARMA I will not be able (to my understanding) to use the flow measurement as a predictor for the production. 
 A: VAR models are routinely used with seasonal data, e.g. in macroeconomics where most of the time series (such as GDP or unemployment) are seasonal. Seasonality is handled either (1) outside of the model (by seasonally adjusting the series before fitting a VAR model) or (2) within the model (by including seasonal dummy variables, for example). 
For (1), seasonal decomposition can be performed by function stl, decompose (as mentioned in another answer by @GD_N) or by fitting a univariate SARIMA model or an ARIMA model with seasonal dummies or Fourier terms - but there are other options, too.
For (2), seasonal dummies can be included as exogenous regressors or via the optional argument season in the vars::VAR function in R (scroll down in the package manual for details).
A: Let me explain you in steps on removing seasonality:


*

*Detect the trend: first find if the time series is additive or multiplicative

*Detrend the time series: this will expose seasonality.

*Average seasonality: from the detrend time series, it’s easy to compute the average seasonality. We add the seasonality together and divide by the number of seasonality.


If you are using R, there are two functions, decompose and stl, which help you do the above said. Often, the decomposition is used to removes the seasonal effect from a time series. It provided a cleaner way to understand the trend.


*

*Note 1: you can use the autocorrelation function to identify the seasonality (weekly, monthly, quarterly, half-yearly or yearly)

*Note 2: SARMA handles seasonality, read on it too.

