You're reading from Data Forecasting and Segmentation Using Microsoft Excel Perform data grouping, linear predictions, and time series machine learning statistics without using code

Product type Paperback

Published in May 2022

Publisher Packt

ISBN-13 9781803247731

Length 324 pages

Edition 1st Edition

Languages

C++

Tools

Excel

Concepts

Data Analysis

Author (1):

Fernando Roque

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Table of Contents (19) Chapters

Preface

1. Part 1 – An Introduction to Machine Learning Functions

2. Chapter 1: Understanding Data Segmentation FREE CHAPTER

3. Chapter 2: Applying Linear Regression

4. Chapter 3: What is Time Series?

5. Part 2 – Grouping Data to Find Segments and Outliers

6. Chapter 4: Introduction to Data Grouping

7. Chapter 5: Finding the Optimal Number of Single Variable Groups

8. Chapter 6: Finding the Optimal Number of Multi-Variable Groups

9. Chapter 7: Analyzing Outliers for Data Anomalies

10. Part 3 – Simple and Multiple Linear Regression Analysis

11. Chapter 8: Finding the Relationship between Variables

12. Chapter 9: Building, Training, and Validating a Linear Model

13. Chapter 10: Building, Training, and Validating a Multiple Regression Model

14. Part 4 – Predicting Values with Time Series

15. Chapter 11: Testing Data for Time Series Compliance

16. Chapter 12: Working with Time Series Using the Centered Moving Average and a Trending Component

17. Chapter 13: Training, Validating, and Running the Model

18. Other Books You May Enjoy

Summary

In this introduction to time series forecasting, we learned that we need the data to autocorrelate to be useful for predictions. We have to plot the data and see whether it has seasonal or cyclical trends to learn whether past data influences the next period of data. Then, we need to use the Durbin-Watson statistical test to prove that the data is autocorrelated.

The forecast calculation uses the trending line of the regression model multiplied by the seasonal irregularity factor. This factor gives us the direction of the trending line based on the cyclical information of the past data. Use your experience to analyze whether the forecast returned by the model makes sense with the past data.

In the next chapter, we will start studying grouping statistics.

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Table of Contents (19) Chapters

Summary

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Table of Contents (19) Chapters

Summary

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