Forecasting Models with R

Learn main forecasting models from basic to expert level through a practical course with R statistical software.
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  • Lectures 40
  • Length 6.5 hours
  • Skill Level All Levels
  • Languages English
  • Includes Lifetime access
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About This Course

Published 1/2016 English

Course Description

Course video lectures, content slides and script code files constantly updated (latest: November 2016, audio re-editing)

Learn forecasting models through a practical course with R statistical software 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 R

  • Read data files and perform statistical computing operations by inputting instructions from R script files on the RGui console.
  • Compute simple benchmarking methods such as random walk.
  • Recognize time series patterns with moving averages and exponential smoothing (ETS) methods.
  • Assess if time series is first order trend stationary or constant in its mean.
  • Estimate time series conditional mean with autoregressive integrated moving average (ARIMA) models.
  • Define models’ parameters and evaluate if forecasting errors are white noise.
  • Select best methods and models automatically by comparing information loss criteria.
  • Test methods and 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 40 lectures and 6.5 hours of content. It’s designed for all forecasting models knowledge levels and a basic understanding of R statistical software is useful but not required.

At first, you’ll learn how to read data files and perform statistical computing operations by inputting instructions from R script files in the RGui console. Next, you’ll estimate simple forecasting methods such as arithmetic mean, naïve or random walk, seasonal random walk, random walk with drift and use them as benchmarks against other more complex ones. After that, you’ll evaluate these methods’ forecasting accuracy through mean absolute error, root mean squared error and mean absolute percentage error metrics.

Then, you’ll identify time series level, trend and seasonality patterns through simple moving averages together with Brown’s, Holt’s, Gardner’s, Taylor’s and Winter’s exponential smoothing (ETS) methods. Next, you’ll automatically select best method by comparing several information loss criteria and evaluate these methods’ forecasting accuracy through previously studied error metrics and the introduction of Hyndman and Koehler’s mean absolute scaled error.

After that, you’ll evaluate if time series is first order trend stationary with augmented Dickey-Fuller and Kwiatkowski-Phillips-Schmidt-Shin tests. 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 and partial autocorrelation functions. Later, you’ll automatically select best model by comparing Akaike’s and Schwarz’s Bayesian information loss criteria and evaluate these models’ forecasting accuracy through previously studied errors metrics. Finally, you’ll value if best model’s forecasting errors are white noise with Ljung-Box lagged autocorrelation test and therefore don’t include any predicting information.

What are the requirements?

  • R statistical software is required. Downloading instructions included.
  • Practical example data and R script files provided by instructor.
  • Prior basic R software knowledge is useful but not required.

What am I going to get from this course?

  • Read data files and perform statistical computing operations by inputting instructions from R script files in the RGui console.
  • Compute simple forecasting methods such as naïve or random walk and use them as initial benchmarks.
  • Recognize time series level, trend and seasonality patterns through simple moving averages together with Brown’s, Holt’s, Gardner’s, Taylor’s and Winter’s exponential smoothing (ETS) methods.
  • Assess if time series is first order trend stationary with augmented Dickey-Fuller and Kwiatkowski-Phillips-Schmidt-Shin tests.
  • Estimate time series conditional mean with Box-Jenkins’s autoregressive integrated moving average (ARIMA) models.
  • Define 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 methods and models automatically by comparing Akaike’s and Schwarz’s Bayesian information loss criteria.
  • Test methods and models predicting accuracy by comparing forecasting errors’ metrics such as Hyndman and Koehler’s mean absolute scaled error.

What is the target audience?

  • Students at any knowledge level who want to learn about forecasting models using R statistical software.
  • 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 .CSV data file as external resources.

Article

Before starting section please download .TXT R script 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, R statistical software, RGui (64-bit) console overview, data .CSV file, data sources, R script in .TXT files, statistical computation instructions with R script files).
03:27
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.
03:52
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 bibliography).
04:33

In this lecture you will learn forecasting models definition, time series decomposition, R statistical software download website, RGUI (64-bit) console overview.

18:55
In this lecture you will learn forecasting models .CSV data file, data sources, R script in .TXT files and statistical computation instructions with R script files (FORECAST and TSERIES packages download, LIBRARY() packages loading function, GETWD() and SETWD() working directory functions, READ.CSV() data reading function, PLOT() charting function, WINDOW() data range delimiting function, automatic .TXT script run and SOURCE() automatic .R script run function).
Section 2: Simple Forecasting Methods
Article

Before starting section please download .TXT R script file as additional resources.

8 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, arithmetic mean method, naïve or random walk method, seasonal random walk method, random walk with drift method and forecasting accuracy (mean absolute error MAE, root mean squared error RMSE and mean absolute percentage error MAPE)).

03:02

In this lecture you will learn section lectures’ details and main themes to be covered related to simple forecasting methods (arithmetic mean method, naïve or random walk method, seasonal random walk method, random walk with drift method and forecasting accuracy (mean absolute error MAE, root mean squared error RMSE and mean absolute percentage error MAPE)).

07:01

In this lecture you will learn arithmetic mean method definition and main calculations (MEANF() arithmetic mean method forecasting function, LINES() charting function and SUMMARY() data variable description function).

06:14
In this lecture you will learn naïve or random walk method definition and main calculations (NAIVE() and RWF() naïve or random walk method forecasting functions).
06:15
In this lecture you will learn seasonal random walk method definition and main calculations (SNAIVE() seasonal random walk method forecasting function).
06:09
In this lecture you will learn random walk with drift method definition and main calculations (RWF() random walk with drift method forecasting function).
10:04

In this lecture you will learn forecasting accuracy, mean absolute error MAE, root mean squared error RMSE and mean absolute percentage error MAPE definition and main calculations (ACCURACY() forecasting accuracy function).

Section 3: Moving Averages and Exponential Smoothing Methods
Article

Before starting section please download .TXT R script file as additional resources.

12 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), 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 forecasting methods accuracy (mean absolute scaled error MASE).
07:40
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 forecasting methods accuracy (mean absolute scaled error MASE).
05:51
In this lecture you will learn simple moving average SMA method definition and main calculations (MA() simple moving average calculating function and FORECAST() forecasting function).
06:33
In this lecture you will learn Brown’s simple exponential smoothing method definition and calculation through least squares estimation (SES() simple exponential smoothing function and ETS() all exponential smoothing methods function).
12:00
In this lecture you will learn Holt’s Linear and Exponential trend methods definition and main calculations (HOLT() trend exponential smoothing methods function and ETS() all exponential smoothing methods function).
13:24
In this lecture you will learn Gardner’s additive and Taylor’s multiplicative damped trend methods definition and main calculations (HOLT() trend exponential smoothing methods function and ETS() all exponential smoothing methods function).
17:36
In this lecture you will learn Holt-Winters additive, multiplicative and multiplicative damped methods definition and main calculations (HW() seasonal exponential smoothing methods function and ETS() all exponential smoothing methods function).
14:11

In this lecture you will learn forecasting methods accuracy, method selection and mean absolute scaled errors MASE definitions and main calculations (ETS() automatic best exponential smoothing method selection function, ACCURACY() forecasting accuracy function).

Section 4: Auto Regressive Integrated Moving Average Models
Article

Before starting section please download .TXT R script file as additional resources.

27 pages

In this lecture you can download slides with sections lectures’ details and main themes to be covered related to first order trend stationary time series (autocorrelation function ACF, partial autocorrelation function PACF, augmented Dickey-Fuller ADF unit root test, Kwiatkowski-Phillips-Schmidt-Shin KPSS test, logarithmic time series transformation and time series differentiation), ARIMA model specification (autocorrelation function ACF and partial autocorrelation function PACF), ARIMA models (random walk with drift model, geometric random walk with drift model, first order autoregressive model, differentiated first order autoregressive model, Brown’s simple exponential smoothing model, simple exponential smoothing with growth model, Holt’s linear trend model, Gardner’s additive damped trend model, seasonal random walk with drift model, seasonal random trend model, general seasonal model, general first order autoregressive model, seasonally differentiated first order auto regressive model and Holt-Winters’ additive model), forecasting models accuracy (Akaike information criterion AIC, corrected Akaike information criterion AICc and Schwarz Bayesian information criterion BIC) and best model’s forecasting residuals white noise (Ljung-Box autocorrelation test).

09:27
In this lecture you will learn section lectures’ details and main themes to be covered related to first order trend stationary time series (autocorrelation function ACF, partial autocorrelation function PACF, augmented Dickey-Fuller ADF unit root test, Kwiatkowski-Phillips-Schmidt-Shin KPSS test, logarithmic time series transformation and time series differentiation), ARIMA model specification (autocorrelation function ACF and partial autocorrelation function PACF), ARIMA models (random walk with drift model, geometric random walk with drift model, first order autoregressive model, differentiated first order autoregressive model, Brown’s simple exponential smoothing model, simple exponential smoothing with growth model, Holt’s linear trend model, Gardner’s additive damped trend model, seasonal random walk with drift model, seasonal random trend model, general seasonal model, general first order autoregressive model, seasonally differentiated first order auto regressive model and Holt-Winters’ additive model), forecasting models accuracy (Akaike information criterion AIC, corrected Akaike information criterion AICc and Schwarz Bayesian information criterion BIC) and best model’s forecasting residuals white noise (Ljung-Box autocorrelation test).
19:53
In this lecture you will learn first order trend stationary time series tests definition and main calculations (ACF() autocorrelation function, PACF() partial autocorrelation function, ADF.TEST() augmented Dickey-Fuller unit root test function and KPSS.TEST() Kwiatkowski-Phillips-Schmidt-Shin Test autocorrelation test function). You will also learn differentiation to achieve first order stationary time series definition and main calculations (DIFF() time series differentiation function).
11:01
In this lecture you will learn ARIMA model specification definition and main calculations (ACF() autocorrelation function and PACF() partial autocorrelation function).
18:38
In this lecture you will learn random walk, geometric random walk, random walk with drift and geometric random walk with drift models definitions and main calculations (RWF() random walk model forecasting function and ARIMA() all ARIMA models calculation function).
11:12
In this lecture you will learn first order auto regressive and differentiated first order autoregressive models definitions and main calculations (ARIMA() all ARIMA models calculation function).
11:35
In this lecture you will learn Brown’s simple exponential smoothing and simple exponential smoothing with growth ARIMA models definitions and main calculations (ARIMA() all ARIMA models calculation function).
09:58
In this lecture you will learn Holt’s linear trend ARIMA model definition and main calculations (ARIMA() all ARIMA models calculation function).
07:17
In this lecture you will learn Gardner’s additive damped trend ARIMA model definition and main calculations (ARIMA() all ARIMA models calculation function).
13:14
In this lecture you will learn seasonal random walk with drift and seasonal random trend ARIMA models definitions and main calculations (ARIMA() all ARIMA models calculation function).
12:48
In this lecture you will learn general seasonal and general first order autoregressive seasonal ARIMA models definitions and main calculations (ARIMA() all ARIMA models calculation function).
14:06
In this lecture you will learn seasonally differentiated first order autoregressive and Holt-Winters additive ARIMA models definitions and main calculations (ARIMA() all ARIMA models calculation function).
18:02
In this lecture you will learn forecasting models accuracy, model selection, Akaike information criterion AIC, corrected Akaike information criterion AICc and Schwarz Bayesian information criterion BIC definitions and main calculations (AUTO.ARIMA() automatic best ARIMA model selection function, ACCURACY() forecasting accuracy function).
13:21
In this lecture you will learn forecasting residuals white noise tests definitions and main calculations (RESIDUALS() forecasting errors or residuals calculation function and BOX.TEST() Ljung-Box lagged autocorrelation test function).
Section 5: Bibliography
2 pages
In this lecture you can download slides with course bibliography.

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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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