In this blog, I will  talk about an overview of the time series analysis and their objective, how to analyze the time series, and what are the limitations of it.

What is Time Series Analysis?
One particular method of examining a collection of data points gathered over time is time series analysis. Instead than only sometimes or arbitrarily recording data points, TSA analysts capture the data points at regular intervals throughout a predetermined length of time.

The Objectives of Time Series Analysis: To comprehend the operation of time series and the variables that influence a given variable(s) at different times. The results and insights of the features of the given dataset that vary over time can be obtained through time series analysis. Supporting the process of anticipating the time series variable’s future values.

How to Analyze the Time Series?
Gathering and cleansing the data.
Getting the visualization ready in terms of time versus important feature.
observing the series’ stationarity.
Making charts to comprehend its characteristics.
ARMA, ARIMA, MA, and AR models are built.
drawing conclusions from forecasts.

The limitations of the Time Series Analysis:
Like other models, TSA does not support the missing data.
The relationships between the data points must be linear.
Data transformations are rather costly because they are required.
Most models operate on univariate data.