Forecasting Models with Excel

Learn main forecasting models and methods from basic to expert level through a practical course with Excel.
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  • Lectures 43
  • Length 8.5 hours
  • Skill Level All Levels
  • Languages English
  • Includes Lifetime access
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    Available on iOS and Android
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About This Course

Published 12/2015 English

Course Description

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

  • Estimate simple benchmarking methods such as random walk.
  • Identify time series patterns with moving averages and exponential smoothing methods.
  • Select best methods by comparing forecasting errors’ metrics.
  • Evaluate if time series is first order stationary or constant in its mean.
  • Calculate time series conditional mean with autoregressive integrated moving average (ARIMA) models.
  • Determine models’ parameters and evaluate if forecasting errors are white noise.
  • Choose best models by comparing forecasting errors’ information criteria.
  • Test models’ forecasting accuracy by comparing their predicting capabilities.

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.

What are the requirements?

  • Spreadsheet software such as Microsoft Excel is required.
  • Practical example spreadsheet provided by instructor.
  • Prior basic spreadsheet software knowledge is recommended.

What am I going to get from this course?

  • Estimate simple forecasting methods such as naïve or random walk and use them as benchmarks against other more complex ones.
  • 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.
  • Select best methods by comparing scale-dependent and scale-independent forecasting errors’ metrics such as Hyndman and Koehler’s mean absolute scaled error.
  • Evaluate if time series is first order stationary with deterministic trend and seasonality together with augmented Dickey-Fuller test.
  • Calculate time series conditional mean with Box-Jenkins’s autoregressive integrated moving average (ARIMA) models.
  • Determine models’ parameters with autocorrelation, partial autocorrelation functions and use them to evaluate if forecasting residuals are white noise together with Ljung-Box test.
  • Choose best models by comparing Akaike’s and Schwarz’s Bayesian information criteria.
  • Test forecasting accuracy of methods and models by comparing their predicting capabilities.

What is the target audience?

  • Students at any knowledge level who want to learn about forecasting models.
  • Academic researchers who wish to deepen their knowledge in data science, applied statistics, operations research, economics, econometrics or quantitative finance.
  • Business or financial analysts and data scientists who desire to apply this knowledge in sales and financial forecasting, inventory optimization, demand and operations planning, or cash flow management.

What you get with this course?

Not for you? No problem.
30 day money back guarantee.

Forever yours.
Lifetime access.

Learn on the go.
Desktop, iOS and Android.

Get rewarded.
Certificate of completion.

Curriculum

Section 1: Course Overview
Article

Before starting course please download .XLSX Microsoft Excel file as additional resources.

10 pages

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).

03:21

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.

03:27

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).

08:42

In this lecture you will learn forecasting models definition, time series decomposition, data source for real-world practical example and Excel workbook, worksheets and calculations guide.

Section 2: Simple Forecasting Methods
6 pages

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).

02:46
In this lecture you will learn section lectures’ details and main themes to be covered related to simple forecasting methods (naïve or random walk method, seasonal random walk method, random walk with drift method and seasonal random walk with drift method).
07:13
In this lecture you will learn naïve or random walk method definition and main calculations.
07:47
In this lecture you will learn seasonal random walk method definition and main calculations.
09:24
In this lecture you will learn random walk with drift method definition and main calculations.
13:11
In this lecture you will learn seasonal random walk with drift method definition and main calculations.
Section 3: Moving Averages and Exponential Smoothing Methods
14 pages
In this lecture you can download slides with sections lectures’ details and main themes to be covered related to moving averages (simple moving average method and weighted moving average method), exponential smoothing methods (Brown’s simple exponential smoothing method, Holt’s linear trend method, exponential trend method, Gardner’s additive damped trend method, Taylor’s multiplicative Damped trend method, Holt-Winters additive method, Holt-Winters multiplicative method and Holt-Winters damped method) and method selection (sum of squared errors SSE, mean absolute error MAE, root mean squared error RMSE, mean absolute percentage error MAPE, symmetric mean absolute percentage error sMAPE and mean absolute scaled error MASE).
05:03
In this lecture you will learn section lectures’ details and main themes to be covered related to moving averages (simple moving average method and weighted moving average method), exponential smoothing methods (Brown’s simple exponential smoothing method, Holt’s linear trend method, exponential trend method, Gardner’s additive damped trend method, Taylor’s multiplicative Damped trend method, Holt-Winters additive method, Holt-Winters multiplicative method and Holt-Winters damped method) and method selection (sum of squared errors SSE, mean absolute error MAE, root mean squared error RMSE, mean absolute percentage error MAPE, symmetric mean absolute percentage error sMAPE and mean absolute scaled error MASE).
06:42
In this lecture you will learn simple moving average SMA method definition and main calculations.
13:10
In this lecture you will learn weighted moving average WMA method definition and main calculations.
07:36
In this lecture you will learn Brown’s simple exponential smoothing method definition and calculation through least squares estimation.
11:45
In this lecture you will learn Holt’s linear trend method definition and calculation through least squares estimation.
11:10

In this lecture you will learn exponential trend method definition and calculation through least squares estimation.

12:23
In this lecture you will learn Gardner’s additive damped trend method definition and calculation through least squares estimation.
12:13
In this lecture you will learn Taylor’s multiplicative damped trend method definition and calculation through least squares estimation.
17:03
In this lecture you will learn Holt-Winters additive method definition and calculation through least squares estimation.
15:48
In this lecture you will learn Holt-Winters multiplicative method definition and calculation through least squares estimation.
17:25
In this lecture you will learn Holt-Winters damped method definition and calculation through least squares estimation.
19:45
In this lecture you will learn method selection metrics definition and calculation (scale-dependent: sum of squared errors SSE, mean absolute error MAE, root mean squared error RMSE, and scale independent: mean absolute percentage error MAPE, symmetric mean absolute percentage error sMAPE and mean absolute scaled error MASE).
Section 4: Auto Regressive Integrated Moving Average Models
23 pages
In this lecture you can download slides with sections lectures’ details and main themes to be covered related to first order stationary time series (deterministic trend test, deterministic seasonality test, augmented Dickey-Fuller unit root test on the mean), ARIMA model specification (autocorrelation function and partial autocorrelation function), ARIMA models (geometric random walk with drift model, first order auto regressive model, differentiated first order auto regressive model, Brown’s simple exponential smoothing ARIMA model, simple exponential smoothing with growth model, Holt’s linear trend ARIMA model, Gardner’s additive damped trend ARIMA model, seasonal random walk with drift model, seasonal random trend model and seasonally differentiated first order auto regressive model) and model selection (Akaike information criterion AIC, corrected Akaike information criterion AICc, Schwarz Bayesian information criterion BIC and forecasting errors Ljung-Box autocorrelation white noise test).
12:11
In this lecture you will learn section lectures’ details and main themes to be covered related to first order stationary time series (deterministic trend test, deterministic seasonality test, augmented Dickey-Fuller unit root test on the mean), ARIMA model specification (autocorrelation function and partial autocorrelation function), ARIMA models (geometric random walk with drift model, first order auto regressive model, differentiated first order auto regressive model, Brown’s simple exponential smoothing ARIMA model, simple exponential smoothing with growth model, Holt’s linear trend ARIMA model, Gardner’s additive damped trend ARIMA model, seasonal random walk with drift model, seasonal random trend model and seasonally differentiated first order auto regressive model) and model selection (Akaike information criterion AIC, corrected Akaike information criterion AICc, Schwarz Bayesian information criterion BIC and forecasting errors Ljung-Box autocorrelation white noise test).
19:23
In this lecture you will learn first order trend stationary time series tests definition and main calculation (deterministic trend and augmented Dickey-Fuller unit root test on the mean). You will also learn differentiation to achieve first order stationary time series.
14:16
In this lecture you will learn first order seasonal stationary time series test definition and main calculation (deterministic seasonality with dummy variables).
18:53
In this lecture you will learn ARIMA model specification definition and main calculation (auto correlation function and partial auto correlation function).
17:53
In this lecture you will learn geometric random walk with drift model definition and main calculations.
10:10
In this lecture you will learn first order auto regressive model definition and calculation through least squares regression.
15:02
In this lecture you will learn differentiated first order auto regressive model definition and calculation through least squares regression.
14:11
In this lecture you will learn Brown’s simple exponential smoothing ARIMA model definition and calculation through least squares estimation.
14:52
In this lecture you will learn simple exponential smoothing with growth model definition and calculation through least squares estimation.
19:47
In this lecture you will learn Holt’s linear trend ARIMA model definition and calculation through least squares estimation.
14:38
In this lecture you will learn Gardner’s additive damped trend ARIMA model definition and calculation through least squares estimation.
14:21
In this lecture you will learn seasonal random walk with drift model definition and calculation.
06:10
In this lecture you will learn seasonal random trend model definition and calculation.
12:59
In this lecture you will learn seasonally differentiated first order auto regressive model definition and calculation through least squares regression.
16:26
In this lecture you will learn model selection metrics definition and calculation (Akaike information criterion AIC, corrected Akaike information criterion AICc, Schwarz Bayesian information criterion BIC).
15:33
In this lecture you will learn forecasting residuals white noise tests definition and calculation (auto correlation function, partial autocorrelation function and Ljung-Box auto correlation Q statistic).
Section 5: Forecasting Accuracy
3 pages

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.

15:22
In this lecture you will learn forecasting accuracy tests definition and calculation (best fitting methods and models predictive capabilities).

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Instructor Biography

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 finance and data analysis. Within finance he has focused on stock fundamental, technical and investment portfolio analysis. Within data analysis he has concentrated on applied statistics, probability, optimization methods, forecasting models and machine learning. 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.

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