Book Image

Data Forecasting and Segmentation Using Microsoft Excel

By : Fernando Roque
Book Image

Data Forecasting and Segmentation Using Microsoft Excel

By: Fernando Roque

Overview of this book

Data Forecasting and Segmentation Using Microsoft Excel guides you through basic statistics to test whether your data can be used to perform regression predictions and time series forecasts. The exercises covered in this book use real-life data from Kaggle, such as demand for seasonal air tickets and credit card fraud detection. You’ll learn how to apply the grouping K-means algorithm, which helps you find segments of your data that are impossible to see with other analyses, such as business intelligence (BI) and pivot analysis. By analyzing groups returned by K-means, you’ll be able to detect outliers that could indicate possible fraud or a bad function in network packets. By the end of this Microsoft Excel book, you’ll be able to use the classification algorithm to group data with different variables. You’ll also be able to train linear and time series models to perform predictions and forecasts based on past data.
Table of Contents (19 chapters)
1
Part 1 – An Introduction to Machine Learning Functions
5
Part 2 – Grouping Data to Find Segments and Outliers
10
Part 3 – Simple and Multiple Linear Regression Analysis
14
Part 4 – Predicting Values with Time Series

Doing the forecast

To calculate the forecast of a time series, we have to multiply the seasonal irregularity by the trending data of the regression model. With this, we will have the ups and downs of the seasonal irregularity in our forecast. These results are explained in the following figure:

Figure 3.7 – Forecast

The preceding figure shows the components of the forecast of our time series. We have the seasonal or cycling data with the up-trending line to do a forecast of the multiplication of the seasonal irregularity trend that we discussed before.

Like the regression model, the time series forecast just gives us an idea of what could happen in the future; it is not an exact prediction. For example, we can see that for the fourth semester of year 11, we will see a growth in passenger demand. This makes sense with the past data showing an increased passenger demand in the fourth trimesters of almost all the past years. The relatively low increment...