Measurement of Seasonal Variation by Different Methods

We must find ways of separating and measuring seasonal changes. There are two motives for separating and measuring the impact of seasonal variations.

To examine the changes caused by seasons on the values of a particular variable in a time series, Remove this variable from the series of times to determine its value. By summing the results of a specific season over several years, the irregular fluctuations will be cancelled out because of independent random fluctuations. If we eliminate the influence of cyclical and trend fluctuations in the season, seasons’ variations are excluded and are calculated in percentages from their mean. Analysing seasonal variations will help you plan more realistically for purchases, production, etc. This article will examine the types of seasonal variation and their application.

Types of seasonal variation

There are four types of seasonal variation. These are:

1. Simple averages types

2. Ratio to the types of trends

3. Percentage moving averages are types of

4. Link related types

In this regard, we will look at the design of seasonal indexes using only the first type.

Here are some methods that we can use to gauge the trends.

(i) Freehand or Graphic Method.

(ii) The Semi-Average Method.

(iii) The Method for Moving Averages.

(iv) Method of Least Squares.

What are Measurements of Trends in statics?

After analysing the types of seasonal variation, now we will discuss the measurement of trends in statistics. 

Different tools are available to look at patterns in data. They range from basic (like linear regression) to more sophisticated tools such as the Mann-Kendall Test, which can be used to find non-linear patterns. Other tools that are popular include:

Autocorrelation analysis is when error terms from a time series change from one time period to another.

Curve-fitting: A useful technique to model particular patterns. For instance, you can attempt to fit the growth curve of Gompertz’s distribution to your data.

A filtering process or smoothing reveals trends from the noisy data, while smoothing adds weight (i.e., greater importance) to the latest data.

The Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test determines if a time series is stationary around a mean or linear trend or is non-stationary due to a unit root.

The MANCOVA (Multivariate Analysis covariance) is the more multivariate variant of ANCOVA. It will tell you if the group differences could have occurred by random chance or if there’s an observable trend.

The Seasonal Kendall test (SK test) analyses data for monotonic trends within seasonal patterns.

Transformations: creating a new time series by combining already existing data. It is useful for, for example, eliminating the linear trend.

Measuring seasonal variations

This is the most straightforward and most efficient method of studying Seasonal Variations. The process of the basic average method is described in the following.

Seasonal fluctuations are periodic series that repeat on a more or less frequent basis throughout the year or even every year. The term “periodic series” is only used in theory; however, seasonal variations are very similar to the model. The model’s assumption design is that regular series are

result of the systematic and not caused by accidental or systematic causes that are not accidental.

Procedure:

(i) Sort the data by quarters, months, or years based on the information provided.

(ii) (iii) Find out the sum of every quarter, month, or year.

(iii) Calculate the quarter’s median, month, or the whole year.

(iv) Determine the median of the averages

 (iv) This is referred to as Grand Average (G)

(v) Compute Seasonal Index for each season( (i.e.) quarters, months, or a year) 

(vi) If the information is given in the months

We can also calculate SI for the rest of the months.

(vii) If information is provided in the quarter

Conclusion

The statistical data derived from time or dynamic series is different from

other data sets arranged according to the time variable. This is where the problem lies.

The significance of studying the time and the significance of studying the time. It is possible to determine the importance of studying the time series. The significance of studying time series. Most of the time, they are a major factor in the socio-economic environment as well as other fields. In the real world, most of the data is the subject of investigation in both social and economic spheres. They are available in the form of time series. A key component of the time series is the trend. Cyclical, Random oscillations and random oscillations can be described as seasonal fluctuations.