In the previous session you have studied about two Auto Regressive models. These models were the most vanilla models in the world of Auto Regressive models. In this session, you will hop on to more complex AR models and learn how they can be helpful in forecasting time series with great accuracies. The models that you will learn in this session are –
- Auto-Regressive Moving Average (ARMA).
- Auto-Regressive Integrated Moving Average (ARIMA).
- Seasonal Auto-Regressive Integrated Moving Average (SARIMA).
- Seasonal Auto-Regressive Integrated Moving Average with Exogenous variable (SARIMAX).
Note
In this session, you are expected to work extensively on Python to forecast the time series data using different Auto Regressive techniques. You will be working on different real-life datasets. Follow the given instruction on splitting the dataset into train and test data as mentioned. The parameters should also be used as given in the instruction or wrong parameters can lead you to a different answer.
For each of the models that you will learn, we have provided some optional model building assessments as well. If you feel you need some practice on building these models in Python apart from the main airline traffic dataset that you are working on, we would recommend solving these questions. They have been provided at the end of the module as an optional session; additionally, we have also provided the links to the appropriate assessments in each segment so that you can directly jump onto the assessment once you are done learning about a model.
The airline passenger traffic dataset used throughout this session is given below:
Guidelines for in-module questions
The in-video and in-content questions for this module are not graded. Note that graded questions are given on a separate page labelled ‘Graded Questions’ at the end of this session. The graded questions in this session will adhere to the following guidelines:
First Attempt Marks | Second Attempt Marks | |
Question with 2 Attempts | 10 | 5 |
Question with 1 Attempt | 10 | 0 |
People you will hear from in this session
Subject Matter Expert
Deputy General Manager – Data Science, Mahindra Group
Chiranjoy is a data science and artificial intelligence leader at Mahindra Group. Before Mahindra, Chiranjoy has worked at McKinsey and JP Morgan Chase in customer, operations and risk analytics. He holds a Masters degree in Operations Research from IIT Bombay.