Now in this video we will be discussing about the different components of time series or time series depicts the relationship between two variables time is one of those variables and the second is any quantitative variable it is not necessarily that the relationship always shows increment in the change of the variable with reference to time the relation is not always decreasing, it may be increasing for some and decreasing for some other points of that time series analysis helps to predict the future behavior of the variable based on past experience it is helpful for business planning as it helps in comparing the actual current performance with the expected one from time series analysis we get to study the past behavior of the phenomena or the variable and the Constitution we can also compare the changes in the values of different variables at different times of places a graphical representation of a time series reveals the changes over time.

Usually we should come across time series in showing continual changes over time giving us an overall impression of our opposite critical study of the series reveals that the changes are not totally haphazard, at least a part of it can be accounted for the part of it the top part of the time series which can be accounted for is known as a systemic part of the series. The systematic part of the series includes the trend cyclical and seasonal component and the unsystematic part or the remaining part is the irregular component other random components the various reasons are the forces, which affects the values of an observation in a time series are the components of a time series, the four categories of the components of the time series are first, the trend component, next is a cyclical component. Next is a seasonal component. Next is the random component.

Now let's first do the trend or the security company. The secular trend is the main component of a time series which Results from long term effects of socio economic and political factors. This trend may show the growth or decline in a time series over a long period. This is the type of tendency which continues to persist for a very long period of time. So by secular trend we mean any smooth long term movement of the series if observed for a sufficient long period of time any series may exhibit an upward trend or downward trend, the trend is the long term pattern of a time series or trends can be positive or negative, depending on whether the time series exhibits an increasing long term pattern or decreasing long term. For example, if we want to see the trend of the production of steel in the last 20 years in India, it is an upward trend then CD or crude death rate in India for the last 15 years.

It's a downward This is a graphical representation of secular trend. Now let's move to the cyclical variation or the cyclical component. cyclical components are long term oscillations occurring in a time series these oscillations are more observed in economics data and the periods of such oscillations are generally extended from five to 12 years or more. So it's a type of non periodic movement. These oscillations are associated with the well known business cycles, these cyclic movements can be studied provided a long series of measurements. So, cyclical variation is an oscillator movement in a time, where the period of time is generally more than one year and also after movement is not strictly periodic because it oscillates the period intensity may vary from one site to another any pattern showing an up and down movement around a given trend is identified a cyclical better.

The best example for cyclical variation is a business cycle, legging business cycle, there is always a boom or recession or depression or recovery phase, a prosperity phase these phases always happens in the business cycle, but it never happens at same point of time for every business cycle or fraud, every business for this varies so it is a non periodic movement. The amount of recession in a particular form will vary from out of recession from other folks. So, that is a non periodic movement where the intensity varies from one site to another. Therefore, this component is called a cyclical component This is a graphical representation of cyclical variation is a cyclical component next to seasonal variation. Seasonal copies are short term movements operating data due to seasonal factors the short term is generally considered as a period in which changes occurs in a time series with variations in weathers or festivities.

For example, it is commonly observed that the consumption of ice cream during summer in Kolkata is generally high and hence, an ice cream dealer sales would be higher in some months of the year but relatively lower during winter months in Kolkata. Similarly, the sale of garments umbrellas, greeting cards and fireworks are subject to large variations during festivals like Valentine's Day, eat Christmas, New Year's, etc. So seasonal variation is when a particular event is happening at the same point of time. Every year, so the variation is only occurring at that point of time every year is seasonal variation. For example, the sale of clothes during Durga Puja and Kolkata is always very high. So that's a seasonal variation.

So seasonality occurs when the time series exhibits regular fluctuations during the same month or every year, same month or month every year or during the same quarter every year. It's a periodic movement because it's happening at the same point of time. In a series a period is generally less than one year. A periodic movement is a movement which repeats itself after a constant interval for example, sale of bullet floats in Kolkata during the month of December or sale of clothes during the month of October. This is the season we're here represented concept of seasonal variation. Graphically, this graph is representing the sales of baseball softball events, the sale of different sporting goods.

Now let's move to the concept of random components. Random components are the irregular part of the time series data or the unsystematic part of time series data that remains uncaptured. So random component components are also called irregular fluctuations. So random components are certain changes or sudden changes occurring in a time series which are unlikely to be repeated. They are components of time series which cannot be explained by trends, seasonal or cyclical movements. These variations are sometimes called residual or random component is variations, though accidental in nature can cause a continual change in the trend seasonal cyclical oscillations during the forthcoming period, for example, floods, fires, earthquakes, revolutions, epidemic strikes are the root causes of such irregularities.

So, this is the most irregular random part of the series which causes mainly due to some unforeseen events like for example, labor problems, natural catastrophe like flat then Brexit. Brexit is also random component. This component is unpredictable which results in random variation in prediction. The objective is to model the component in such a way that the only component that remains unexplained is the random components. So here I've represented the four components of time series graphically is a long term trend. This is long term trend with cyclical variations.

Long term trend with cyclical and seasonal variation long term trend with cyclical seasonal and random components or movements. So in this video we will be learning till here. So for now, let's end this video over here. Goodbye. Thank you see you work for the next week.