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

Building and training the forecast model

We are going to use the car sales for the years 2012 to 2015 to design and train the time-series forecast model. We saw in the previous section that this data has autocorrelation. The steps for designing the model are as follows:

Calculate the moving average for two quarter periods.
Get the center moving average of the preceding step.
Calculate the separation between the quarter sales and the center moving average for each record. This separation between the quarter sales and center moving average is the fluctuation of sales over time.
Get the seasonal trend for each record by averaging the fluctuation for each quarter.
Compute the trend with the regression line of the quarterly sales.
Calculate the forecast by multiplying the season trend by the regression for each record.

Calculate the moving average and the center moving average for the data to get the chart shown in Figure 13.4. The center moving average...

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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

Table of Contents (19) Chapters

Building and training the forecast model

Authors (1)

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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

Table of Contents (19) Chapters

Building and training the forecast model

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