Time series analysis – Deciphering long-term trends and seasonal variations 

Time series analysis – Deciphering long-term trends and seasonal variations 

With the advances made in technology, every business strives to stay ahead of its game! Businesses tend to predict their future performance and look for opportunities for growth and improvement. Likewise, an organization uses multiple forecasting techniques to make informed decisions about the future and plan budgets beforehand.

Time series analysis is a forecasting model generally used by auditors, financial analysts, accountants, and company managers to understand financial information and interpret future trends and patterns based on historical data. If you are intrigued and want to gain in-depth knowledge regarding ACCA’s F2 topic, ‘Time series analysis,’ then keep reading.

Components of time series

To be able to make a more accurate forecast, the time series is decomposed into four fundamental components, which help take various factors into account that influence the data’s behavior. These components are :

• Trend (T)

The trend component represents the gradual increase or decrease in the data over a long period of time. The trend is a general and smooth average tendency that either rises, falls, or remains stable over time. It is usually upwards or downwards and helps understand the underlying growth or decline in time series. It assists in indicating many tendencies of movement like population growth or decline, temperature trends, agricultural production, and much more. 

The trend, when plotted on a graph, is either linear or non-linear. Linear means when the set of data clusters is straight, and non-linear means when it is curvilinear. Refer to the lectures of our most dedicated teacher, Ahmed Shafi, on MHA’s LMS portal to ace your F2 exam. 

• Seasonal variation (S)

Seasonal variations are the periodic and rhythmic shifts in the data, occurring at regular intervals. They are recorded daily, weekly, quarterly, monthly, or yearly and are influenced by external factors like seasons or holidays. For instance, the sale of air conditioners boosts in summer. 

• Cyclical variation (C)

Cyclical variations exhibit patterns that are longer than a year or typical seasonality. These cycles are irregular and are influenced by economic factors. The cyclic movement is also referred to as the ‘Business Cycle.’ It consists of four phases, which are boom, recession, depression, and recovery. A business studies economic fluctuation and cyclical patterns to make budgets and forecasts.

• Random variable (R)

These are irregular variations that are unforeseeable, unpredictable, erratic, and unmanageable. It does not have a pattern and can not be attributed to seasonality or trend. It is triggered by natural or man-made disasters like floods, earthquakes, famine, etc. 

Models of time series

Two models are used to separate the four components (trend, seasonal, cyclical, and random) that affect the variables and have a relative effect on the time series.   

• Additive model

The additive model supposes that each component acts independently. It assumes that the time series can be represented as a sum of all four components. This model is often used when the seasonal variations are constant, and the trend is linear. It is expressed as follows:  Y(t)= T(t)+S(t)+C(t)+R(t).

• Multiplicative model

It is an alternative approach to the additive model and assumes that the components act proportionately to each other. It is preferred over the additive model when the seasonal variation is not constant and becomes proportionately larger as the time series value increases. The model is presented as follows: Y(t) = T(t)*S(t)*C(t)*R(t).

Example

Here is an example of a question that ACCA might test in your exam:

Question:

A time series model of sales volume has the following trend and additive seasonal variation:

Y= 4000 + 6000X,

Where Y is the quarterly sales volume in units, and X is the quarter number (the first quarter of 2017 = Quarter 1, the second quarter = Quarter 2, etc.)

Seasonal Variation:

What would be the time series forecast of sales units for the second quarter of 2018?

Answer:

Y= 4000 + 6000X

Y= 4000 + 6000(6)

Y= 40000 + 1000

Y= 41000

If you want to master the skill of calculating accurate time series forecasts, then immediately enroll in our F2 course taught online and onsite by Sir Ahmed Shafi at Mirchawala’s Hub of Accountancy.

Conclusion

Time series analysis is a versatile analytical technique that helps in interpreting the data collected over a period of time. It helps multiple businesses to forecast the future behavior of a variable based on past data. The decision-making process becomes more efficient, and precise judgments are made.

Hope this article helped you better comprehend the topic of time series and all your queries are now cleared. Visit the MHA LMS portal and watch Sir Ahmed Shafi’s lectures to get a professional guide for your ACCA F2 exam. 

Frequently Asked Questions (FAQ’s)

Q1) What are the four components of time series?
The four components of the time series are as follows:

• Trend: the long-term movement of data over time.
• Seasonal variation: the repetitive fluctuations at regular intervals in data.
• Cyclical variation: the cyclical patterns are longer than a year and are influenced by economic factors.
• Random variation: one-off events like floods.

 Q2) What is the difference between time series and regression?

Regression assumes that the data is independent and random, while time series is ordered and dependent on time.

Q3) What are the applications of time series analysis?

There are various practical uses for time series, like sales forecasting, weather forecasting, economic forecasting, finance forecasting, and more.

Q4) What are the limitations of time series analysis?

Time series analysis might give less accurate results due to missing and irregular data. Also, selecting an appropriate model for time series is a complex task and would require specialist expertise. 

Q5) What is the concept of time series?

A time series is a collection of clearly defined data points produced and recorded over time. They are taken on a regular, timely basis to study and analyze the change in one variable at successive points in time.

Written by Yumna Tariq Bright student of Mirchawala’s Hub Of Accountancy 

Visit ACCA Global for further information.