# A Deep Dive into Forecasting- Excel & R.

Forecasting with Excel & R. how to forecast 100000 time series at once? use them to be the forecaster for the Business

Language: english

Note: 4.5/5 (47 notes) 14,327 students

Instructor(s): Haytham Omar

Last update: 2021-12-06

## What you’ll learn

• Time Series Decomposition.
• Univariate analysis for time series
• Bivariate analysis and auto-correlation
• Smoothing the time series
• seasonally adjusting the time series
• Generating and Calibrating Forecasting in Excel
• Learning R and using it as everyday tool for forecasting
• Using the Fable Package for advanced forecasting methods and aggregations
• Time Series Forecasting
• Different Applications of forecasting
• R
• Fable
• Excel

• Nop

## Description

Hello đź™‚

Forecasting has been around for 1000s of years. it stems from our need to plan so we can have some direction for the future. We can consider forecasting as the stepping stone for planning. and that’s why it is as important as ever to have good forecasters in institutions, supply chains,  companies, and businesses.

With the ever-growing concerns of sustainability and Carbon-footprint. Would you believe it? a good forecast actually contributes to saving resources through the value chain and actually saving the planet. one forecaster at a time. needless to mention, forecasting is integral in marketing, operations, finance, and planning for supply chains…. pretty much everything

This course is aimed to orient you to the latest statistical forecasting techniques and trends. but first, we need to understand how forecasting works and the reasoning behind statistical methods, and when each method is suitable to be used.  that’s why we start first with excel and we scale with R. “Don’t worry if you don’t know R, Crash fundamental sections are included!.

the course is for all levels because we start from Zero to Hero in Forecasting.

in this course we will learn and apply :

1- Time Series Decomposition in Excel and R.

2- Univariate analysis for time series in Excel and R.

3- Bivariate analysis and auto-correlation in Excel and R.

4- Smoothing the time series and getting the Trend with Double and centered moving average.

5- seasonally adjusting the time series.

6- Simple and complex forecasts in Excel.

7- Use transformations to reduce the variance while forecasting.

8-Generating and Calibrating Forecasting in Excel.

9- Learning R and using it as an everyday tool for forecasting.

10- Using the Fable Package for advanced forecasting methods and aggregations.

11- Using Forecast package for grid search on ARIMA.

12- Applying a workflow of different models in two lines of code.

13- Calibirating forecasting methods.

14- Applying Hierarchical time series with Bottom-up, middle out, and Top-down Approaches.

16-  Use the new R-Fable reconciliation method for aggregation.

15- Using Fable to generate forecasts for 10000  time-series and much more !!

*NOTE: Many of the concepts and analysis I explain first in excel as I find excel the best way to first explain a concept and then we scale up, improve and generalize with R. By the end of this course, you will have an exciting set of skills and a toolbox you can always rely on when tackling forecasting challenges.

Happy Forecasting!

Haytham

Rescale Analytics

Feedback from Clients and Training:

“In Q4 2018, I was fortunate to find an opportunity to learn R in Dubai, after hearing about it from indirect references in UK.

I attended a Supply Chain Forecasting & Demand Planning Masterclass conducted by Haitham Omar and the possibilities seemed endless. So, we requested Haitham to conduct a 5-day workshop in our office to train 8 staff members, which opened us up as a team to deeper data analysis. Today, we have gone a step further and retained Haitham, as a consultant, to take our data analysis to the next level and to help us implement inventory guidelines for our business. The above progression of our actions is a clear indication of the capabilities of Haitham as a specialist in R and in data analytics, demand planning, and inventory management.”

Shailesh Mendonca

â€ś Haytham mentored me in my Role of Head of Supply Chain efficiency. He is extremely knowledgebase about the supply concepts, latest trends, and benchmarks in the supply chain world. Haythamâ€™s analytics-driven approach was very helpful for me to recommend and implement significant changes to our supply chain at Aster groupâ€ť

Saify Naqvi

â€śI participated to the training session called “Supply Chain Forecasting & Management” on December 22nd 2018. This training helped me a lot in my daily work since I am working in Purchase Dpt. Haytham has the pedagogy to explain us very difficult calculations and formula in a simple way. I highly recommend this training.â€ť

Djamel BOUREMIZ

Purchasing Manager at Mineral Circles Bearings

## Who this course is for

• Planners
• Strategists
• Retail merchandise
• Financiers
• Supply chain
• Economists
• Operation managers
• Budgeters

## Course content

• Introduction
• Hello.
• Forecasting is the stepping stone of planning
• Time Series
• Difficulties in forecasting
• Forecasting applications
• Forecasting in inventory management
• Different Forecasting Methods
• 2020 and COVID
• Time Series analysis
• Causal Methods
• Stationarity of the data
• Summary
• Quiz on Chapter 1
• Time Series and Pattern extraction
• Introduction
• Univariate Statistical analysis
• Univariate Part2
• Bivariate Statistics
• Auto-Correlation
• Assignment
• Assignment Solution
• Summary
• Simple Forecasting Methods
• Simple Forecasting methods
• Naive and Seasonal Naive
• Mean Percentage error
• Seasonal average
• Mean absolute scaled error
• Simple exponential smoothing and log transformations
• Simple forecasting Methods
• Naive and Simple forecasting methods
• linear Regression , Custom weighted moving average and SES
• Optimizing the Paremeters
• Best Simple Forecasting Method
• Simple Forecasting assignments
• Solution
• Summary
• Double Moving average, Centered Moving average and Decomposition.
• Introduction
• Moving averages
• Detrending the series
• Time series Decomposition
• Multiplicative Decomposition
• Assignment
• Decomposition solved
• Summary
• Exponential Smoothing
• Introduction
• Simple Exponential Smoothing
• Holt Exponential Smoothing
• Initialization of alpha and Beta
• Holt Model in Excel
• Holt-winters Explanation
• 12 month Forecast with Holt Winters
• Multiplicative Holt-Winters
• 12 Month ahead with multiplicative exponential smoothing
• Assignment Holt
• Assignment Solution
• Multiple Linear Regression
• Introduction
• Intro to linear regression
• Multiple linear regression in excel
• Fitting the model
• shifting to R
• Welcome to R
• Welcome to the World of R!
• What is R statistical language?
• How to install R
• How to install Rstudio
• A walkthrough tutorial
• install packages!
• Summary
• R fundmentals
• Introduction- r-Basics
• Different Data Structures and types in R
• Do arithmetic Calculations in R and write vectors
• Creating a list
• Importing Data in R and basic Exploration functions
• Selecting Data in dataframe.
• If Else function
• Conditions
• Functions with conditions
• For loops
• applying a function inside a forloop
• for loop on a data frame
• Applying a function on a dataframe
• Assignment
• Assignment section 4 answer Part1
• Assignment Section 4 answer Part 2
• Summary
• Working with dates in R
• Intro
• Motivation for working with dates
• Parsing Dates with R
• Make inference from dates in R
• Working with lubridate
• Modeling inter-arrival time of customers
• Modeling inter arrivai time of customers2
• Assignment
• Assignment question 1 o 4
• Assignment answer question 5 and 6
• assignment last question
• Summary
• Time series forecasting with R
• Forecasting with R
• Preparing the data for regression
• Changing the format of posixct to date
• Fitting forecast regression with R
• Multiple regression with R
• Assignment
• assignment solution part 1
• Assignment solution part 2
• Summary
• Converting data to timeseries
• Weekly and daily time series
• Analyze the time series
• Seasonal Components
• Time series decomposition in R
• Measuring strength of trend and seasonality
• Exponential smoothing
• Arima and it’s components
• Accuracy measures for forecasting
• Determine Arima orders
• Training and testing
• Dynamic harmonic regression
• Measuring accuracy of new model
• Improving ARIMA with grid search
• Error handling while grid search
• Battle of the ARIMAs
• Assignment
• Summary
• Advanced Multiple Forecasting with Fable
• Fable
• Evolution of Forecasting
• Making a Tsibble
• ACF with Fable
• Time Series Decomposition
• Double Moving average with Fable
• Measuring Trend and seasonality Strength
• Fitting multiple models with a workflow
• Generating a new test set
• Comparing linear and non-linear models
• Statistical methods workflow
• Testing accuracy
• Multiple time series fitting
• Multiple time-series accuracy
• Decomposition models
• Prophet Model
• Prophet models in R
• Prophet conclusion
• VAR models
• VAR in R
• Var Conclusion
• Assignment Fable
• Fable Assignment 1
• Multiple Linear Fable assignment
• Assignment conclusion
• Forecasting Aggregations with Fable
• Aggregations
• Hierarchal and grouping
• Aggregation approaches
• Making A hierarchal structure with Tsibble
• Crossing the aggregations
• Manual aggregations
• Reconcile
• Middle out and Top Down
• Minimum Trace method forecasts
• Forecasting for two years
• Accuracy on all levels
• Final notes

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