RA- Deep Dive Into Forecasting – Excel and Python.

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 Python, 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 Python.

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

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

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 Python  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 Python.. 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

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