by Conrad Carlberg | Released July 2015 | ISBN: 9780134197135

In this easy-to-follow video course, learn how to forecast trended time series accurately in Excel: the foundation for a wide range of powerful predictive analytics applications! DescriptionCompanies of all sizes are turning to exponential smoothing to accurately forecast trended data such as sales, demand, and other key business indicators. In this video course, world-class analytics expert Conrad Carlberg shows you how to use smoothing to forecast trends with a tool you already know: Microsoft Excel. Carlberg illuminates each technique through easy-to-follow video, with crystal-clear explanations reflecting his decades of experience solving complex analytical problems with Excel. You'll learn how smoothing works and how to prepare data; quantify a forecast's accuracy; use Excel Solver to reduce forecast error; interpret smoothing analyses; work with baselines; support your forecasts with regression analyses; diagnose trends using autocorrelation; detrend and forecast from a trended baseline; initialize forecast values; and backcast beyond the start of your baseline. You'll learn hands-on through practice workbooks provided for your own use and adaptation. By the time you're done, you'll have mastered one of today's most valuable predictive analytics skillsets—one you can use in nearly any field of business. About the InstructorConrad Carlberg is a multiple recipient of Microsoft's Most Valuable Professional (MVP) award for Microsoft Excel. He has written 11 books about quantitative analysis with Excel, including Predictive Analysis: Microsoft Excel and Statistical Analysis: Microsoft Excel. His company (at conradcarlberg.com) specializes in custom statistical and analytical problems ranging from inventory control to real-estate market segmentation. Carlberg holds a Ph.D. in statistics from the University of Colorado and has 25 years of experience in applying advanced analytic techniques. Skill LevelIntermediateAdvancedWhat You Will LearnHow to use powerful trended smoothing techniques in Excel to predict sales, demand, and moreHow to use Excel's Solver to optimize values and constants in order to minimize forecasting errorHow to convert time series observations and forecasts to charts that make your predictions intuitively clearHow trended smoothing techniques work in the familiar context of Excel syntax and worksheetsHow to adapt this video's accompanying workbooks to your own unique requirementsWho Should Take This CourseDirect client statistical modelers and others who need to perform advanced analytics and data mining to identify booking opportunities and plan for customer retentionDatabase marketing analysts working with customer-related metricsSenior marketing reporting analysts and others who must deliver production reporting and analytics for retail marketing, product, and/or finance business partnersDigital Analytics VPs and others working at or with advertising agencies to develop data-driven digital marketing insight products and optimization approaches for client engagementsAnyone seeking more effective ways to predict sales and/or demandCourse RequirementsAssumes some knowledge of regression analysis (or at least familiarity with the contents of a book such as Conrad Carlberg's Statistical Analysis: Microsoft Excel 2013About LiveLessons Video TrainingLiveLessons Video Training series publishes hundreds of hands-on, expert-led video tutorials covering a wide selection of technology topics designed to teach you the skills you need to succeed. This professional and personal technology video series features world-leading author instructors published by your trusted technology brands: Addison-Wesley, Cisco Press, IBM Press, Pearson IT Certification, Prentice Hall, Sams, and Que. Topics include IT Certification, Programming, Web Development, Mobile Development, Home and Office Technologies, Business and Management, and more. View all LiveLessons on InformIT at http://www.informit.com/livelessons. Show and hide more

1. Introduction
   • Introduction 00:04:38
2. Lesson 1 Simple Exponential Smoothing: A Review
   • Learning Objectives 00:01:01
   • 1.1 Prepare data for exponential smoothing 00:04:07
   • 1.2 Carry out a simple exponential smoothing analysis 00:16:29
   • 1.3 Quantify the accuracy of forecasts 00:15:52
3. Lesson 2 Smoothing and Its Notation
   • Learning Objectives 00:01:21
   • 2.1 Optimize forecasts using Excel's Solver add-in 00:12:17
   • 2.2 Interpret smoothing analyses presented in various sources 00:13:16
   • 2.3 Resolve apparent discrepancies in smoothing equations 00:13:39
4. Lesson 3 Characteristics of Trend in a Time Series
   • Learning Objectives 00:01:56
   • 3.1 Understand why simple exponential smoothing works poorly with a trended series 00:13:13
   • 3.2 Interpret a correlation coefficient in terms of standard scores 00:13:07
   • 3.3 Understand how autocorrelation helps to characterize baselines 00:13:01
5. Lesson 4 Diagnosing Trend with Least Squares
   • Learning Objectives 00:01:23
   • 4.1 Use LINEST() and TREND() functions to support smoothing forecasts 00:17:20
   • 4.2 Understand the limitations of regression forecasts 00:14:44
   • 4.3 Evaluate a forecasting model by analyzing residuals 00:10:57
6. Lesson 5 Diagnosing Trend: The Autocorrelation Function and the Concept of Lags
   • Learning Objectives 00:01:50
   • 5.1 Distinguish the methods of the ACF from those of the Pearson correlation 00:11:30
   • 5.2 Use the ACF add-in to create and interpret ACF correlograms 00:10:15
   • 5.3 Interpret correlograms to help evaluate borderline cases 00:05:01
7. Lesson 6 Differencing
   • Learning Objectives 00:01:10
   • 6.1 Detrend a baseline using first differences 00:07:20
   • 6.2 Forecast the baseline's first differences 00:07:51
   • 6.3 Reintegrate the forecast differences into the baseline 00:04:50
8. Lesson 7 The Forecast Equation for Trend
   • Learning Objectives 00:01:22
   • 7.1 Use Holt's double exponential smoothing method to forecast trended time series 00:09:51
   • 7.2 Use either the smoothing or the error correction formulas to forecast from a trended baseline 00:13:14
   • 7.3 Use defined names and relative references to derive self-documenting formulas 00:24:22
9. Lesson 8 Initializing Values
   • Learning Objectives 00:01:21
   • 8.1 Employ standard methods to initialize forecast values 00:09:43
   • 8.2 Backcast via smoothing to Period 0 in a stationary baseline 00:10:20
   • 8.3 Backcast via smoothing to Period 0 in a trended baseline 00:07:44
10. Summary
    • Summary 00:02:37
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