You’ve probably heard someone say that a commodity’s price has increased or decreased over time. This commodity could be gold, silver, food, gasoline, diesel, and so on. You might also be well versed with the skyrocketing bank interest rates. However, the interest rate on home loans has dripped down. What exactly are all of these terms? What are the advantages they provide to us?
The time series of data is a sort of data. We would learn about components of time series, as well as components of time series analysis. So let’s discuss this topic in detail.
Time Series
How do people find out if the price of a commodity has risen over time? They can do so by comparing the commodity’s pricing over a period of time. A time series is a set of observations presented in chronological order.
To put it in another way, a time series is a collection of data organised by the time of occurrence. It refers to the manner in which the data is presented. In this situation, time is only a way of linking the entire phenomenon to proper reference points. Hours, days, months, and years are all examples of time.
The relationship between two variables is depicted by a time series. One of those factors being time, and the other being any quantitative variable. It is not essential for the relationship to constantly reflect an increase or a decrease in the variable’s change with respect to time. It may be rising for some and falling for others at different times.
Do you have any examples in mind? One such example would be the temperature of a specific city during a particular week or month.
Time Series Uses
The most essential benefit of studying time series is that it allows us to estimate how a variable will behave in the future based on previous experiences.
It is useful for business planning since it allows you to compare current performance to expected performance.
We can study the previous behaviour of the phenomena or variable under consideration using time series.
Components of Time Series Analysis
The components of time series are the many factors and forces that affect the values of an observation in a time series. The four components of time series are as follows:
Trend
Seasonal Variations
Cyclic Variations
Random or Irregular movements
Trend
The trend depicts the data’s overall propensity to increase or decrease over a long period of time. A trend is an average, smooth, long-term tendency. It is not always necessary for the rise or drop to be in the same direction during a specific time period.
In different periods of time, the tendency might develop, decrease, or remain unchanged. The overall pattern, on the other hand, must be upward, downward, and stable. Population, agricultural production, manufactured commodities, birth rates and mortality rates, number of industries or factories, and number of schools or colleges are only a few examples of movement patterns.
Linear and Non-Linear Trend
If we plot the values of the time series on a graph in relation to time t, the data clustering pattern reveals the type of trend. The trend is linear if the data clusters roughly around a straight line; else, it is non-linear.
Periodic Fluctuations
In a time series, some elements tend to repeat themselves after a given amount of time has passed. They behave in a spasmodic pattern.
Seasonal Variations
These are the rhythmic forces that work in a predictable pattern during a period of less than a year. Throughout a 12-month period, they have almost the same pattern. If the data is captured hourly, daily, weekly, quarterly, or monthly, this variance would be visible in the time series.
Natural forces or man-made conventions are both responsible for these differences. Seasonal fluctuations are influenced by the different seasons or climatic conditions. The sale of umbrellas or raincoats increases during the rainy season, whereas the sale of electric fans and air conditioners increases during the summer.
Cyclic Variations
Cyclic variations are those fluctuations in a time series data that occur over a longer period of time, essentially more than a year. This cyclic action oscillates for a year. One era is made up of a cycle. The ‘Business Cycle’ is the name given to this cyclical movement.
The four-phase cycles are prosperity, recession, depression, and recovery. Although the cyclic variation is regular, it is not periodic. The nature of the economic forces and their interaction determine the ups and downs in the industry.
Irregular or Random Movements
Another factor that contributes to the fluctuation in this variable under study. They are not regular variations, but rather random and irregular variances. Unexpected, unmanageable, unpredictable, but erratic variations characterise these swings. Earthquakes, wars, floods, famines, and other natural disasters are examples of these forces.
Conclusion
The elements which cause changes in a time series, are known as time series components. A time series, in particular, enables you to see what causes influence specific variables from one period to the next. Time series analysis is important for determining how an asset, security, or economic variable changes with time. In both fundamental and technical analysis, time series forecasting methods have been applied. I hope now you understand all about the components of time series. For better understanding kindly read the article thoroughly.
