Time Series Analysis

Definition of Time Series Analysis

Time series refers to an arrangement and presentation of statistical data in chronological order. The statistical data is collected over a period of time. According to Spiegel, “A time series is a set of observations taken at specified times, usually at equal intervals.” There exist various forces that affect the values of the phenomenon in a time series. These are also the components of the time series analysis. Learn the definition of Time Series Analysis here.

Definition of Time Series Analysis

Following are the various components of the time series:

  1. Secular Trend or Simple trend or Long term movement: Secular trend refers to the general tendency of data to increase or decrease or stagnate over a long period of time. Time series relating to Economic, Business, and Commerce may show an upward or increasing tendency. Whereas, the time series relating to death rates, birth rates, share prices, etc. may show a downward or decreasing tendency.
  2. Seasonal variations: Seasonal variations refer to the changes that take place due to the rhythmic forces which operate in a regular and periodic manner. These forces usually have the same or most similar pattern year after year. When we record data weekly, monthly or quarterly, we can see and calculate seasonal variations. Thus, when a time series consists of data only based on annual figures, there will be seen no seasonal variations. These variations may be due to seasons, weather conditions, habits, customs or traditions. For example, in summers the sale of ice-cream increases and at the time of Diwali the sale of diyas, crackers, etc. go up.
  3. Cyclical variations: Cyclical variations are due to the ups and downs recurring after a period from time to time. These are due to the business cycle and every organization has to phase all the four phases of a business cycle some time or the other. Prosperity or boom, recession, depression, and recovery are the four phases of a business cycle.
  4. Random or irregular variations: Random variations are fluctuations which are a result of unforeseen and unpredictable forces. These forces operate in an absolutely random or erratic manner and do not have any definite pattern. Thus, these variations may be due to floods, famines, earthquakes, strikes, etc.

Definition of Time Series Analysis

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Question on Time Series Analysis

What are the advantages of using Time series analysis?

Answer:

The advantages of time series analysis are as follows:

  1. Reliability: Time series analysis uses historical data to represent conditions along with a progressive linear chart. The information or data used is collected over a period of time say, weekly, monthly, quarterly or annually. This makes the data and forecasts reliable.
  2. Seasonal Patterns: As the data related to a series of periods, it helps us to understand and predict the seasonal pattern. For example, the time series may reveal that the demand for ethnic clothes not only increases during Diwali but also during the wedding season.
  3. Estimation of trends: The time series analysis helps in the identification of trends. The data tendencies are useful to managers as they show an increase or decrease in sales, production, share prices, etc.
  4. Growth: Time series analysis helps in the measurement of financial growth. It also helps in measuring the internal growth of an organization that leads to economic growth.
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