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What is Time Series Analysis ?
The method of studying the features of the response variable with respect to time as the independent variable is called time series analysis. Use the time variable as a point of reference to estimate the target variable in the name of predicting or forecasting.
“A time series is nothing but a sequence of various data points that occurred in a successive order for a given period of time”
Steps to analyze Time Series
Collect data and clean it
visualize the data with respect to time vs key feature
Observe the stationarity of the series
Build the charts to understand its pattern
Build the model - AR, MA, ARMA and ARIMA
Extract useful information from prediction
Line charts are used to illustrate time series. These are commonly used in business forecasting, earthquake prediction, weather forecasting, Econometrics, control engineering, communication engineering, astronomy, and other fields. Time series analysis is a powerful tool that statisticians can use to make conclusions and develop appropriate tactics. Statisticians use a variety of methodologies to investigate time series. There are three types of methods: linear and non-linear methods, parametric and non-parametric methods, and univariate and multivariate methods.
Application of Time series :
Descriptive analysis: This will determine the trend and pattern in the time series.
Spectral analysis: This is a frequency domain that divides a time series into periodic and cyclical components. For example, you can track changes in the sales of a certain product.
Forecasting : Businesses utilise this extensively to learn about changes and forecast future possibilities.
Intervention analysis: It helps in identifying the event that has the potential to alter the time series. For example, compare an employee's performance before and after training.
Explanative Analysis : This will help you to understand about the relationship between time series and their dependence on one another.
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