The analysis of time series opens new ways of development

Considering the analysis of time series not as an abstract statistical concept, but as a phenomenon widely used in practice, we can conclude that this topic is very relevant for today to study a number of processes. Especially it is in demand in the economic activity of man, therefore most examples in popular science literature are given precisely from the point of view of its use in this context. But the scope of the study and evaluation of time series does not end there.

The very definition of a time series in many respects reminds us of the process of collecting any statistical information, and consists in the precise fixation of real indicators at certain intervals of time, measured in a way that gives the greatest reliability. In other words, when describing any phenomenon, a graph is used where the time indices of the measurement are fixed on the abscissa axis and its real physical quantities on the ordinate axis.

In fact, time series analysis methods formed the basis for describing many physical laws and technical processes. Their generalization allowed the process of description to be reduced to a certain mathematical expression. But not all processes could fit into the framework of clear formulas. But the solution of two main problems has not been canceled. They are:

- determination of the nature of the series;

- forecasting.

So the analysis of time series received additional stimulus to its development, and in its arsenal appeared a rich set of tools, methods.

A classic example of a time series was the series proposed in 1976 by Box and Jenkins. On the example of studying the activity of monthly international air transport for twelve years in the period 1949-1960, they showed the presence of two components: a practically linear trend and seasonal changes. When traffic growth has steadily increased, and depending on the season, there have been occasional spots of splash and decay of activity. This type of description was called the model with multiplicative seasonality.

In the same year, the same Box and Jenkins offered very interesting in terms of forecasting, but a very laborious and complex method of the Autoregressive Integrated Secondary Moving Average (ARPSS).

When studying processes that are subject to external influences, dissemination received a practical method of interrupted time series. It was described in the 80s of the last century. The essence of the method is to study the processes after intervention in the system from the outside. The analysis of time series was to assess the introduction of new management methods, the use of various know-how, the impact of lawmaking processes , etc.

Spectral analysis of time series appeared on the basis of previous methods. Among the evaluation criteria for this method is clearly visible period and frequency. Complex numbers, Fourier transforms are used quite widely in calculations.

The abundance of methods and methods that involve the analysis of time series, confirms how fertile this soil is for further research. After all, the descriptions of these processes are cumbersome and require a certain amount of experience from the analyst. A powerful leap in the development of personal computing technology led to the conclusion of this type of analysis to a new qualitative level. And the ubiquitous dissemination of the Internet has made available the results of recent studies in this field available for a broad category.

What, as not a time series analysis, uses a successful player in the Forex market, it is the study of the company's development schedules that allows the leader to develop the correct strategic line, and the market assessment provides an extensive field of activity for marketers and managers, allowing to adjust the price level and range of products or services sold The purpose of obtaining the maximum benefit.

Each method of analysis deserves special attention and requires thorough study. And if you are interested in at least one of them, then the goal of the article is achieved.