#### 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.
Preface
Part 1 – An Introduction to Machine Learning Functions
Free Chapter
Chapter 1: Understanding Data Segmentation
Chapter 2: Applying Linear Regression
Chapter 3: What is Time Series?
Part 2 – Grouping Data to Find Segments and Outliers
Chapter 4: Introduction to Data Grouping
Chapter 5: Finding the Optimal Number of Single Variable Groups
Chapter 6: Finding the Optimal Number of Multi-Variable Groups
Chapter 7: Analyzing Outliers for Data Anomalies
Part 3 – Simple and Multiple Linear Regression Analysis
Chapter 8: Finding the Relationship between Variables
Chapter 9: Building, Training, and Validating a Linear Model
Chapter 10: Building, Training, and Validating a Multiple Regression Model
Part 4 – Predicting Values with Time Series
Chapter 11: Testing Data for Time Series Compliance
Chapter 12: Working with Time Series Using the Centered Moving Average and a Trending Component
Chapter 13: Training, Validating, and Running the Model
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# Chapter 12: Working with Time Series Using the Centered Moving Average and a Trending Component

The concept behind making a forecast with a time series is that a factor variable for each period determines whether a trend value goes up or down. The constraint of this prediction is that values must be autocorrelated, meaning that present values are dependent on past values. A prerequisite of making a forecast is a test of autocorrelation, such as the Durbin-Watson probe that we reviewed in the previous chapter.

Once we have validated the autocorrelation of data, we smooth the peaks of the periods using the moving average and the Centered Moving Average (CMA). The distance between the data and the CMA determines the factor that will give the forecast combined with the trend or linear regression of the data.

In this chapter, we will learn how to detect autocorrelation by reviewing the timeline data chart and determining whether it is worth using the Durbin-Watson statistical test...