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

Summary

In this chapter, we learned that we have to produce a chart of time-series data as a first step to see whether our data is a good fit for forecasting. The data has to have autocorrelation (meaning that there is a relationship between past and present values) for us to be able to forecast with it. We can use the Durbin-Watson statistical test to see whether data has autocorrelation or not. The approach of Durbin-Watson is to research the periodicity of errors or residuals to see whether they have predictable behavior. Most products' sales probably do not have periodic behavior, making it difficult for planners to predict optimal inventory management.

In the next chapter, we will learn how to smooth time-series peaks with a Moving Average (MA). We will combine the Centered Moving Average (CMA) with the residuals of linear regression to build a forecast model.