Forecasting has become very important to all business enterprises and individual as well. This book provides readers with the tools to analyze their data, develop forecasting models and present the results in Excel.

Step-by-step development of the forecasting techniques are presented in a clear, simple and straightforward style. The essential text covers techniques that include but not limited to time series moving average, exponential smoothing, Box-Jenkins (ARIMA), Monte Carlo simulation, neural network, K Nearest Neighbors and so on. And unlike other books of its kind that require either high-priced statistical software or Excel add-ins, this book does not require such software. It can be used both as a primary text and as a supplementary text. Apart from that

  • Only minimal mathematic knowledge is require

  • Lots of Excel spreadsheet examples

  • Provides a wide range of proven methodologies

  • Includes both time series and causal models

This book is also ideal for a small business owner or someone who may not have the budget for expensive analytical tools such as SAS or SPSS. If you are building your forecast based on time series analysis or independent variables, then this book will be perfect for you.

The chapter includes:

Chapter 1: Basic Forecasting Methods                      

 Moving Average – Forecast a country farm production

 Exponential Smoothing – Forecast a country farm production

 Holts Method – Forecast a winter clothing sales

 Holts Winters – Forecast fishing rods sales

Chapter 2: ARIMA / Box Jenkins

 Arima Modeling

 Subjective Method

 Example 1: Daily Sales Forecasting

 Objective Method

 Example 2: Forecast daily electricity production

Chapter 3: Monte Carlo Simulation                           

 Example 1: Coin Tossing

 Example 2: Sales Forecasting

 Example 3: Modeling Stock Prices

Chapter 4: K Nearest Neighbors                               

 Example 1: KNN for classification of home ownership

 Example 2: KNN for time series prediction with stock market data

 Example 3: Cross Validation With MSE

Chapter 5: Neural Network With MS Excel               

 Example 1: Credit Approval Model

 Example 2: Sales Forecasting

 Example 3: Predicting Stock Prices (DJIA)

 Example 4: Predicting Real Estate Value

 Example 5: Classify Type  Of  Flowers

Chapter 6: Markov Chain with MS Excel                   

 Example 1: Predict delivery time for a courier company

 Example 2: Predict brand loyalty of soft drink customers

Chapter 7: Bayes’ Theorem  with MS Excel             

 Example 1: Predict rainy or sunny day

 Example 2: Predict stock market trend

The objective of this book is to provide the reader with the tools to analyze data and produce forecasts in MS Excel. Written in a simple, straightforward style, Powerful Forecasting With MS Excel presents basic statistical techniques using practical business examples to teach readers how to predict short, medium and long-term forecasts.

Each chapter explores a specific forecasting topic and takes you forward into more advanced areas until you are able to understand powerful forecasting using Excel spreadsheet.

Once you are comfortable with the practicalities of using the spreadsheet models then perhaps you can simply tailor one of the preexisting spreadsheets for your own use. However, if you need to develop your own unique model then you will find that this book has the materials that you can reference to build one on your own. Assuming little prior knowledge, this informal text is a great companion for those who have not studied forecasting in depth before. This book truly promotes self-study as readers are encouraged to tackle problems as they go along and can see fully worked examples to help their understanding. Both beginners and more advanced students will find material in this book relevant to their needs. The book you will read once, and refer to over and over again!

A business intelligence degree program, such as a data mining degree, provides information on how to create business models for analysis and forecasting.


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