3 Types of Seasonality and How to Detect Them

<h2>&nbsp;</h2> <p>Seasonality is one of the key components that make up a time series. Seasonality refers to systematic movements that repeat over a given period with a similar intensity.</p> <p>Seasonal variations can be caused by various factors, such as weather, calendar, or economic conditions. Examples abound in various applications. Flights are more expensive in the summer because of vacations and tourism. Another example is consumer spending which increases in December due to holidays.</p> <p>Seasonality means the average value in some periods will be different than the average value at other times. This issue&nbsp;causes the series to be non-stationary. This is why it is important to analyze seasonality when building a model.</p> <h1>Three Types of Seasonality</h1> <p>There are three types of seasonal patterns that can emerge in time series. Seasonality can be deterministic or stochastic. On the stochastic side, seasonal patterns can be either stationary or not.</p> <p>These types of seasonality are not mutually exclusive. A time series can have both a deterministic and stochastic seasonal component.</p> <p>Let&rsquo;s describe each pattern in turn.</p> <h2>Deterministic seasonality</h2> <p>Time series with a deterministic seasonality have a constant seasonal pattern. It always recurs in a predictable way, both in intensity and periodicity:</p> <p><a href="https://towardsdatascience.com/3-types-of-seasonality-and-how-to-detect-them-4e03f548d167">Visit Now</a></p> <h2>&nbsp;</h2>