Time series have several characteristics that make their analysis different from other types of data.
- The time series variable (for example, the stock price) may have a trend over time. This refers to the increasing or decreasing values in a given time series.
- The variable may exhibit cyclicity or seasonality. This refers to the repeating cycle over a specific period (such as week, month, etc.) in the time series. A seasonal pattern exists when a series is influenced by seasonal factors (e.g., the quarter of the year, the month, or day of the week). Seasonality is always of a fixed and known period. As an example, if you study the quarterly earnings of a company such as John Deere, it will exhibit seasonality with the earnings peaking during the harvest season.
- The data will have serial correlation between subsequent observations.
- The data will almost always have an irregular component, which is referred to as the White Noise. This is the random variation not explained by any other factor. White Noise is a stationary process with a constant mean and variance.
