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

Designing the time series data model

We are going to explain the general steps to building a time series model using training data. Use your judgment and experience to discern from the chart whether the data is autoregressive before applying the Durbin-Watson statistical test.

The sequential steps required to build the predictive model with time series machine learning are as follows:

  1. Plot the data to inspect the possible autocorrelation relationship.
  2. Use the Durbin-Watson statistical test to see whether the data is autocorrelated.
  3. Calculate the centered moving average of each period lag of the data.
  4. Determine the separation between the data and the centered moving average. This is known as seasonal irregularity.
  5. Get the trending component of the time series using the regression model line.
  6. Multiply the seasonal irregularity value by the trending result to make the forecast.

We use the centered moving average to smooth or to take the general...