When working with time series data, there are several unique characteristics that can be observed. In general, time series tend to exhibit the following characteristics:

• When looking at time series data, it is essential to see if there is any trend. Observing a trend means that the average measurement values seem either to decrease or increase over time. 
• Time series data may contain a notable amount of outliers. These outliers can be noted when plotted on a graph. 
• Some data in time series tends to repeat over a certain interval in some patterns. We refer to such repeating patterns as seasonality.
• Sometimes, there is an uneven change in time series data. We refer to such uneven changes as abrupt changes. Observing abrupt changes in time series is essential as it reveals essential underlying phenomena. 
• Some series tend to follow constant variance over time. Hence, it is essential to look at the time series data and see whether or not the data exhibits constant variance over time. 

The characteristics listed previously help us to make better analyses when it comes to TSA. Now that we know what to see and expect in time series data, it would be useful to see some real examples in action. Next, let's import a real database and perform various TSA methods on it.