Principles of Time Series Analysis in Business Research

1. Principles of Time Series Analysis in Business Research Methods

2. Time-Series Analysis • Time-series analysis represents the statisticalmethodology suitable for the  designs of longitudinalresearch which includes the single subjects. They have been calculated repeatedly at regular intervals along with time. They have checked as exemplar of most of the longitudinal designs.

3. Time Series Data • Time series analysis represents the statistical technique involves  time series data. It is also known as trend analysis. The time series data indicates data in  series of specific timeperiods. The data of one or more variables has been acquired at same point in time.

4. Four Parts of Time Series • They are as follows: • Secular trend narrates the movement of the term. • Seasonal variations shows seasonal changes. • Cyclical fluctuations has been associated with periodical but there are no seasonal variation. • Irregular variations has been the nonrandom sources in the variations of series.

5. Example of The Time Series • The example includes continuous monitoring of the heart rate of person, readings of air temperature on hourly basis. There are regular closing price of the stock of company. We have also come across the rainfall data on monthly basis. The time series analysis has been utilized when we have 50 or more data points in that series.

6. Components of Time Series Analysis • The time series has threecomponentsand they are trend which is the long term direction. Secondly, we have seasonal which has been calendar related movements. Thirdly, we have irregular which has been unsystematic and fluctuation in the short term.

7. Goal of Time Series Analysis • The Time series analysis  has been important to check the nature of a particular asset, economic variable or security modifying along with time. They are utilized to check on the nature of changes related to data point in comparison to the shifts in different variables along with similar time period.

8. Objectives of Time Series Analysis • We have come across two primary goals of the time series analysis. They are going to find out the nature of a particular phenomenon shown by a series of prediction and observations.

9. Difference Between Regression and Time Series • The regressionis going to analyze mean of dependent variable associated with changes in  independent variables. The  time series calculates the data along with particular period of time. There are data points which are going to be plotted in the charts for analysis in the future.

10. Limitation of Time Series Analysis • Time series analysis shows different types of weaknesses. There are problems in generalization from particular study, problem in getting the right measures along with problems with pointing out correct model to show data.

11. Models of Time Series Forecasting • The time series forecasting model has been established by the performance in future prediction. They can explain the reason for a particular prediction. There has been confidence intervals along with the concept of hidden causes in a particular problem.

12. Problems Encountered in Time Series Data • There are a number of time series problems showing observations like one observation every hour, month, day, or year. The time series  shows observations which were not regular over thetime. They can be shared as discontiguous. There is a deficit in uniformity on observations due to missing values.

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