#A Deep Dive into Statistical Forecasting(Excel & R).



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

What you will 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

Business Forecasting

Excel

Description

Hello 🙂

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

Commercial lead-in Adventure AHQ- Sharaf Group

“ 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. Haytham’s analytics-driven approach was very helpful for me to recommend and implement significant changes to our supply chain at Aster group”

Saify Naqvi

Head of Supply Chain Efficiency

“I 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.”

Djamel BOUREMIZ

Purchasing Manager at Mineral Circles Bearings

English
language

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
Additive Decomposiition
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
Additive Holt Winters Model
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
Setup your project
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
Assignment answer Part 1
Assignment answer Part 2
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
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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