Define time series and its characteristics
A time series is a set of observations measured at successive points in time or over successive periods of time. To explain the pattern of data in a time series, it is often helpful to think in terms of five characteristics: trend, seasonal, cyclical, random variation, and irregular (one-time) variation.
A trend is the underlying pattern of growth or decline in a time series. This gradual shifting over time is usually due to such long-term factors as changes in performance, technology, productivity, population, demographic characteristics, and customer preferences.
Seasonal patterns are characterized by repeatable periods of ups and downs over short periods of time. Seasonal patterns usually conform to buyer behavior and different needs over the course of a year, a quarter, and so on.
Cyclical patterns are regular patterns in a data series that take place over long periods of time. A common example of a cyclical pattern is the movement of stock market values during "bull" and "bear" market cycles.
Random variation (sometimes called noise) is the unexplained deviation of a time series from a predictable pattern, such as a trend, seasonal, or cyclical pattern. Random variation is caused by short-term, unanticipated, and nonrecurring factors and is unpredictable.
Irregular variation is a one-time variation that is explainable.
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