Forecasting Models and Time Series for Business in Python

Forecasting Models and Time Series for Business in Python

Welcome to the most exciting online course about Forecasting Models in Python. I will show everything you need to know to understand the now and predict the future.

Forecasting is always sexy - knowing what will happen usually drops jaws and earns admiration. On top, it is fundamental in the business world. Companies always provide Revenue growth and EBIT estimates, which are based on forecasts. Who is doing them? Well, that could be you!

WHY SHOULD YOU ENROLL IN THIS COURSE?

YOU WILL LEARN THE INTUITION BEHIND THE MODELS WITHOUT FOCUSING TOO MUCH ON THE MATH

It is fundamental that you know why a model makes sense and the underlying assumptions behind it. I will explain to you each model using words, graphs, and metaphors, leaving math and the Greek alphabet to a minimum.

THOROUGH COURSE STRUCTURE OF MOST IMPACTFUL ECONOMETRIC TECHNIQUES

The techniques in this course are the ones I believe will be most impactful, up to date, and sought after:

1. Holt-Winters

2. TBATS

3. SARIMAX

4. TensorFlow Structural Time Series

5. Facebook Prophet

6. Facebook Prophet + XGBoost

7. Ensemble approach

WE CODE TOGETHER LINE BY LINE

I will guide you through every step of the way. I will also explain all parameters and functions that you need to use, step by step.

THE FINAL REASON IS THAT YOU PRACTICE, PRACTICE, PRACTICE.

For each algorithm, there is a challenge. This means that each technique has 2 case studies. The goal is that you apply immediately what you have learned. I give you a dataset and a list of actions you need to take to solve it. I think it is the best way to really cement all the techniques in you.

Did I spike your interest? Join me and learn how to predict the future!

Learn Holt-Winters, Arima, Sarimax, Tensorflow Time Series, Facebook Prophet, XGBoost for Demand Planning & Forecasting

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What you will learn

• Holt-Winters
• TBATS
• SARIMAX

Rating: 4.55494

Level: Beginner Level

Duration: 8.5 hours

Instructor: Diogo Alves de Resende