Profile
Settings
Refer your friends
Sign out
Introduction
In the wider scope of statistics, a time series is a sequence that shows discrete-time data. Time series analysis is oftentimes done through a line chart or run chart and it has a varied field of application ranging from statistics to pattern recognition, electroencephalography, communications engineering, mathematical finance, and weather forecasting. Time series analysis, therefore, focuses on the various methods employed to extract statistical data. Other uses of time series analysis can be seen in calculating the tide heights, calculating the number of sunspots, and figuring out the value of the Dow Jones Industrial Average.
Time Series Analysis
Time Series analysis can be divided into two main classes of classification which are time-dependent methods and frequency-domain methods. Through a time series analysis example, the intricacies of the time series will be represented.
Parametric Method | Non-Parametric Method |
Uses Stationary stochastic approach | Uses Spectrum process |
Includes smaller parameters | Include covariance |
It is divided into linear and non-linear type | It is divided into univariate as well as the multivariate type |
Above table shows differences between Parametric and Nonparametric methods of time series analysis
What is Time Series Analysis?
Time Series Analysis can be defined as specific ways in which sequences of data are analysed throughout a period. Time series analysis also ensures reliability and consistency. Mathematically it can be expressed as
Y=a+bx
Where Y represents the predicted value of any given dependent variable, a represents Y-axis when x=0 and y=a, b represents the slope of the line of regression and is a positive value if it is upward and negative when the slope is downwards. And x here represents the independent variable.
Types of Time Series Analysis
There are 3 kinds of time series analyses depending on the utility which are:
- Time Series Forecasting- This type of time series analysis is used for predictive outcomes, especially when it comes to earthquake or weather forecasting. It can predict the possible future based on values that are derived from the previous values. Mathematically it is expressed as
Yt+h/t =lt +hbt
- Interrupted Time Series- This type of time series analysis is utilised while detecting subtle changes in the entire evolution of time series before and after the introduction of the intervention. This is done to study the changes in the variables occurring due to the introduction of the intervention.
- Regressional Analysis- It is used in conjunction with time series analysis in modification to point the links between two or more time series and test those links.
How is Time Series Analysis Different?
Time Series Analysis | Cross-sectional Studies | Spatial Data Analysis | Stochastic Model |
It is a temporal ordering which is natural | There is no natural ordering of specific observations made | Observations are tied to geographical locations | Closer relations of time series |
Heights of tides of oceans | Wages of individuals related to their levels of education | House pricing related to the location of the housing | Derived from past values |
Table 2 shows the differences in Time Series Analysis with other models
Importance of Time Series Analysis
It is also used to predict earthquakes. Most of the data sets that are derived through the use of time series are retrieved and are based on data of the previous values. Other ways in which the importance of time series analysis can be understood are:
- Helps businesses see underlying causes of patterns and trends
- Data visualisation helps to understand and determine seasonal trends
- Analyses the various factors behind the business or other trends and patterns occurring over time
Time Series Analysis Example
To better understand time series, a time series analysis example has been provided. In weather forecasting, the time series analysis is used in server metrics, sensor data, network data, and monitoring of performance applications.
Conclusion
Time series analysis points towards consistent intervals while recording data, unlike other series analyses which focus on randomly collecting data. This type of analysis also shows the different kinds of changes of variables involved in the data over time which is used to adjust the information in the outcome.
