Course video lectures, content slides and Excel file constantly updated (latest: November 2016, audio re-editing)
Learn forecasting models through a practical course with Excel using real world data. It explores main concepts from basic to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or make business forecasting related decisions. All of this while exploring the wisdom of best academics and practitioners in the field.
Become a Forecasting Models Expert in this Practical Course with Excel
Become a Forecasting Models Expert and Put Your Knowledge in Practice
Learning forecasting methods and models is indispensable for business or financial analysts in areas such as sales and financial forecasting, inventory optimization, demand and operations planning, and cash flow management. It is also essential for academic careers in data science, applied statistics, operations research, economics, econometrics and quantitative finance. And it is necessary for any business forecasting related decision.
But as learning curve can become steep as complexity grows, this course helps by leading you through step by step real world practical examples for greater effectiveness.
Content and Overview
This practical course contains 43 lectures and 8.5 hours of content. It’s designed for all forecasting models knowledge levels and a basic understanding of Excel is recommended.
At first, you’ll estimate simple forecasting methods such as naïve or random walk and use them as benchmarks against other more complex ones.
Then, you’ll identify time series level, trend and seasonality patterns through simple and weighted moving averages together with Brown’s, Holt’s, Gardner’s, Taylor’s and Winter’s exponential smoothing (ETS) methods. Next, you’ll select best methods by comparing scale-dependent and scale-independent forecasting errors’ metrics such as Hyndman and Koehler’s mean absolute scaled error.
After that, you’ll evaluate if time series is first order stationary with deterministic trend and seasonality together with augmented Dickey-Fuller test. Next, you’ll calculate time series conditional mean with Box-Jenkins’s autoregressive integrated moving average (ARIMA) models. Then, you’ll determine models’ parameters with autocorrelation, partial autocorrelation functions and use them to evaluate if forecasting residuals are white noise together with Ljung-Box test. And then, you’ll choose best models by comparing Akaike’s and Schwarz’s Bayesian information criteria.
Finally, you’ll test forecasting accuracy of methods and models by comparing their predicting capabilities.
Before starting course please download .XLSX Microsoft Excel file as additional resources.
In this lecture you can download slides with section lectures’ details and main themes to be covered related to course description (objectives, requirements, instructor profile and disclaimer), course overview main sections (simple forecasting methods, moving averages and exponential smoothing methods, autoregressive integrated moving average models and forecasting accuracy) and forecasting models (definition, time series decomposition, data sources, Excel calculations).
In this lecture you will learn which are the course objectives, how you will benefit from it, its previous requirements, my profile as instructor and disclaimer.
In this lecture you will learn that it is recommended to view course in an ascendant manner as each section builds on last one and also does its complexity. You will also study course structure and main sections (simple forecasting methods, moving averages and exponential smoothing methods, autoregressive integrated moving average models and forecasting accuracy).
In this lecture you can download slides with section lectures’ details and main themes to be covered related to simple forecasting methods (simple forecasting methods overview, naïve or random walk method, seasonal random walk method, random walk with drift method and seasonal random walk with drift method).
In this lecture you will learn exponential trend method definition and calculation through least squares estimation.
In this lecture you can download slides with section lectures’ details and main themes to be covered related to forecasting accuracy (best fitting methods and models predictive capabilities) and bibliography.
Diego Fernandez is author of high-quality online courses and ebooks at Exfinsis for anyone who wants to become an expert in financial data analysis.
His main areas of expertise are financial analysis and data science. Within financial analysis he has focused on computational finance, quantitative finance and trading strategies analysis. Within data science he has concentrated on machine learning, applied statistics and econometrics. For all of this he has become proficient in Microsoft Excel®, R statistical software® and Python programming language® analysis tools.
He has important online business development experience at fast-growing startups and blue-chip companies in several European countries. He has always exceeded expected professional objectives by starting with a comprehensive analysis of business environment and then efficiently executing formulated strategy.
He also achieved outstanding performance in his undergraduate and postgraduate degrees at world-class academic institutions. This outperformance allowed him to become teacher assistant for specialized subjects and constant student leader within study groups.
His motivation is a lifelong passion for financial data analysis which he intends to transmit in all of the courses.