Time Series Forecasting Part 3 - Vector Auto Regression Methods

What you'll be able to do

• Understanding the VAR Model formula
• How to check the bi-directional relationship using Granger Causality
• Procedure to building a VAR model in Python correctly
• Interpreting the results of VAR model
• Deeper Understanding of VARMA and VARMAX
• Strategy to find best VAR model
• Strategy to Hyperparameter tuning like Auto ARIMA
• How to introduce Exogenous variable into VARMAX Forecast

1. 01Deep Dive into Multi Time Series Forecasting - Vector Auto Regression21 lessons · 2 h 10 min

   1. What is VAR and When can we use it?20:23
   2. Download Resources1:00
   3. Intuition behind VAR model formula10:58
   4. Building VAR model in Python3:44
   5. Import Dataset2:07
   6. Visualize the time series1:37
   7. Testing Causation using Granger’s Causality Test7:33
   8. Cointegration Test6:40
   9. Split the series into training and testing data2:19
   10. Course Review1:00
   11. Check for the stationary and make Time Series Stationary7:35
   12. How to select the order (p) of VAR model4:53
   13. Train the VAR model of selected order5:56
   14. Check for serial correlation of residual (errors) using Durbin Watson Statistics1:31
   15. How to forecast VAR model using stats model3:08
   16. Invert the transformation to get the real forecast1:17
   17. Plot of Forecast vs Actual0:40
   18. Evaluate the forecast1:03
   19. VARMA Model15:05
   20. VARMAX with Exogenous Data16:20
   21. Auto ARIMA on VARMA for Model Selection15:04
2. 02Practical Advise for succeeding in Time Series Project7 lessons · 36 min

   1. Align your forecast with working group of shareholders7:40
   2. Directions matters more17:59
   3. Choice of Evaluation Matric0:48
   4. Set realistic success criteria2:02
   5. Educate your Stakeholders (Though they might not always listen)1:15
   6. Explain your forecast0:38
   7. Explain drop in the forecast after going live. Have root cause analysis mechanism involving domain expert5:29

What you need

• Basics of Python
• Foundational knowledge of Data Science
• High school maths

Who this course is for

• Data Science Aspirants
• Data Science Professionals
• Software/Data engineers interested in quantitative analysis
• Professionals working with large datasets
• Data analysts, economists, researchers

Your instructor

Prabhakaran S

Principal Data Scientist

My name is Selva, and I am super excited to mentor you on this course!

I head the Data Science team for a global Fortune 500 company and over the last 10 years of my data science experience I've deployed 20+ global products. I'm also the Founder and Chief Author of Machine Learning Plus, which has over 4M annual readers.

I specialize in covering the in-depth intuition and maths of any concept or algorithm. And based on my existing student requests, I've put up the series of courses and projects with detailed explanations, just like on-the-job experience. Hope you love it!

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