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Forecast and Visualize Time Series Data using ARIIMA Modelling

In this project, we will take a look at few examples where we can apply various time series forecasting techniques.
4.54.5

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What will you learn

  • Identification of time series problem
  • Time series visualization
  • Decomposition of time series
  • Transformation of time series data
  • ARIMA modelling
  • Time series model validation

What will you get

  • Access to recording of the complete project
  • Access to all material related to project like data files, solution files etc.

Prerequisites

  • R 3.3.1 or latest R-Studio
  • Python 2.7

Project Description

Time series forecasting has been one of the important area in data science, it is important to predict a variable associated with time elements such as sales, demand, revenue, profit etc. For logistic and supply chain companies, they need to know the exact inventory they need to stock for that they need to predict the demand for future. 

Similarly, people in sales and marketing need to know how much order the customers are going to place so that they can manage their staff. Telecom companies should know how much manpower they need to prepare so that they can handle peak hour traffic etc. In various businesses, at least 5-10 areas where the variable of interest is associated with the time element. 

Let’s look at few examples where we can apply various time series forecasting techniques.

Instructors

 
Pradeepta