Goal: In this module, you will learn about Time Series Analysis to forecast dependent variables based on time. You will be taught different models for time series modelling such that you analyse a real time dependent data for forecasting.

Objective: At the end of this module, you should be able to:
• Explain Time Series Analysis (TSA)
• Discuss the need of TSA
• Describe ARIMA modelling
• Forecast the time series model

Topics:
• What is Time Series Analysis?
• Importance of TSA
• Components of TSA
• White Noise
• AR model
• MA model
• ARMA model
• ARIMA model
• Stationarity
• ACF & PACF

Hands on:
• Checking Stationarity
• Converting a non-stationary data to stationary
• Implementing Dickey Fuller Test
• Plot ACF and PACF
• Generating the ARIMA plot
• TSA Forecasting