Components of Time Series
Time series can be decomposed into four components, each expressing a particular aspect of the movement of the values of the time series.
They are: Trend, Seasonality, Cycles, Irregularities
Seasonal and Cyclic Variations are the short-term fluctuations, whereas the trend is long-term movements and irregularities are unknowns.
- Trend – this refers to the overall long term direction of the series
The trend shows the general tendency of the data to increase or decrease during a long period of time. It can either be upward, downward or stable depending on the tendency. The population, agricultural production, items manufactured, number of births and deaths, are some of the example showing some kind of tendencies of movement.
2. Seasonality – this refers to the repeated behavior of data which occurs at regular intervals
The seasonality tends to repeat itself over a certain period of time. They almost have the same pattern during a period of 12 months. This variation will be present in a time series if the data are recorded hourly, daily, weekly, quarterly, or monthly.
Due to natural conditions like climatic changes or seasons, we can get this type of variations in time series. Few examples of this: production of fruits/crops depend on seasons, the sale of umbrella and raincoats will be high in the rainy season, and the sale of electric fans and A.C. shoots up in summer seasons.
The effect of man-made conventions such as some festivals, holiday season recurs themselves year after year. During such periods, the sales and prices go up.
3. Cycles — this occurs when a series follows an up-and-down pattern that is not seasonal
The series is likely cyclical if the variations are based on previous values of the series rather than directly on time. For example, when the value of stocks goes up, it gives confidence in the market, so more people invest making prices go up, and vice versa, therefore stocks show a cyclical pattern.
4. Irregularities — this refers to strange dips or jumps in a series.
These variations are unforeseen, uncontrollable, unpredictable, and are erratic. Some of the examples are earthquakes, wars, floods, famines, and any other disasters. Currently, the pandemic is also a very good example of this variation. During this pandemic, many businesses either incurred a huge loss or made more profit. For example, online sales have really gone up whereas local shop owners faced a huge loss because of a decrease in sales.
Some of the examples of Time Series that I mentioned earlier are represented as graphs for better understanding.