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
Example 1: Daily Sales Forecasting
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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