Well let me explain you in steps on removing seasonality:

• 1.Detect the trend:First find if the time series is additive or multiplicative

• 2.Detrend the time series: this will expose seasonality.

• 3.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.

well if you are using R  , there are two functions decompose () and stl() ,which helps 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.

• note1:you can use auto correlation function to identify the seasonality(weekly , monthly , quarterly , half yearly or yearly)

• note2:SARMA handles seasonality , read on it too.

Let me explain you in steps on removing seasonality:

1. Detect the trend: first find if the time series is additive or multiplicative
2. Detrend the time series: this will expose seasonality.
3. 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.