Ra- Deep Dive Into Forecasting - Excel And Python.

Forecasting with Excel & Python. Machine learning and statistical forecasting for Supply Chain.

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 Python and using it as everyday tool for forecasting.

Using the sktime Package for advanced forecasting methods and aggregations.

Time Series Forecasting.

Different Applications of forecasting.

Python

Arima

Machine learning forecasting

hierarchal forecasting

Excel

Requirements

Nop

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 everythingThis 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!HaythamRescale AnalyticsFeedback from Clients and Training:

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Forecasting is the stepping stone of planning

Lecture 3 Time Series

Lecture 4 Difficulties in forecasting

Lecture 5 Forecasting applications

Lecture 6 Forecasting in inventory management

Lecture 7 Different Forecasting Methods

Lecture 8 2020 and COVID

Lecture 9 Time Series analysis

Lecture 10 Causal Methods

Lecture 11 Stationarity of the data

Lecture 12 Summary

Section 2: Time Series and Pattern extraction

Lecture 13 Introduction

Lecture 14 Univariate Statistical analysis

Lecture 15 Univariate Part2

Lecture 16 Bivariate Statistics

Lecture 17 Auto-Correlation

Lecture 18 Assignment

Lecture 19 Assignment Solution

Lecture 20 Summary

Section 3: Simple forecasting methods

Lecture 21 Simple Forecasting methods

Lecture 22 Naive and Seasonal Naive

Lecture 23 Mean Percentage error

Lecture 24 Seasonal average

Lecture 25 Mean absolute scaled error

Lecture 26 Simple exponential smoothing and log transformations

Lecture 27 Simple forecasting Methods

Lecture 28 Naive and Simple forecasting methods

Lecture 29 linear Regression , Custom weighted moving average and SES

Lecture 30 Optimizing the Parameters

Lecture 31 Best Simple Forecasting Method

Lecture 32 Simple Forecasting assignments

Lecture 33 Solution

Lecture 34 Summary

Section 4: Double Moving average, Centered Moving average and Decomposition.

Lecture 35 Introduction

Lecture 36 Moving Averages

Lecture 37 De-trending series

Lecture 38 Time-series Decomposition

Lecture 39 Additive Decomposition

Lecture 40 Multiplicative Decomposition

Lecture 41 Assignment

Lecture 42 Decomposition Solved

Lecture 43 Summary

Section 5: Exponential Smoothing

Lecture 44 Introduction

Lecture 45 Simple Exponential Smoothing

Lecture 46 Holt Exponential Smoothing

Lecture 47 Initialization of alpha and Beta

Lecture 48 Holt Model in Excel

Lecture 49 Holt-winters Explanation

Lecture 50 Additive Holt Winters Model

Lecture 51 12 month Forecast with Holt Winters

Lecture 52 Multiplicative Holt-Winters

Lecture 53 12 Month ahead with multiplicative exponential smoothing

Lecture 54 Assignment Holt

Lecture 55 Assignment Solution

Section 6: Multiple linear Regression

Lecture 56 introduction

Lecture 57 Intro to linear regression

Lecture 58 Multiple linear regression in excel

Lecture 59 Fitting the model

Lecture 60 Shifting to Python

Section 7: Welcome to Python

Lecture 61 Python!

Lecture 62 downloading Anaconda

Lecture 63 Installing Anaconda

Lecture 64 Spyder overview

Lecture 65 Jupiter Notebook overview

Lecture 66 Python Libraries

Lecture 67 Summary

Section 8: Python Programming fundmentals

Lecture 68 Intro

Lecture 69 Dataframes

Lecture 70 Arithmetic Calculations with Python

Lecture 71 Lists

Lecture 72 Dictionaries

Lecture 73 Arrays

Lecture 74 Importing data in Python

Lecture 75 Subsetting Data Frames

Lecture 76 Conditions

Lecture 77 Writing functions

Lecture 78 mapping

Lecture 79 for loops

Lecture 80 for looping a function

Lecture 81 Mapping On a data frame

Lecture 82 for looping on a data frame

Lecture 83 Summary

Lecture 84 Assignment

Lecture 85 Assignment answer 1

Lecture 86 Assignment answer 2

Section 9: working with dates in Python

Lecture 87 Dates intro

Lecture 88 datetime

Lecture 89 Last purchase date and recency

Lecture 90 recency histogram

Lecture 91 Modeling inter-arrival time

Lecture 92 Modeling inter-arrival time 2

Lecture 93 Modeling inter-arrival time 3

Lecture 94 Resampling

Lecture 95 rolling time series

Lecture 96 rolling Time series 2

Lecture 97 Summary

Lecture 98 Assignment

Lecture 99 Assignment answer

Section 10: Statistical Forecasting in Python

Lecture 100 Introduction

Lecture 101 Time Series Intro

Lecture 102 Accuracy Measures

Lecture 103 Preparing the data for time-series

Lecture 104 Getting the time series components: Lecture

Lecture 105 Getting the time series components

Lecture 106 components uses

Lecture 107 Arima Models

Lecture 108 Stationarity test in python

Lecture 109 Arima in python

Lecture 110 ARIMA diagnostics

Lecture 111 Grid search

Lecture 112 For looping ARIMA

Lecture 113 error handling

Lecture 114 fitting the best model

Lecture 115 Mean absolute error

Lecture 116 Arima Comparison

Lecture 117 Exponential smoothing

Lecture 118 Exponential smoothing in python

Lecture 119 Comparing exponential smoothing models

Lecture 120 Time series summary

Lecture 121 Assignment.

Lecture 122 Assignment Explanation 1

Lecture 123 assignment explanation 2

Lecture 124 Assignment explanation 3

Lecture 125 Assignment Explanation 4

Section 11: Machine learning forecasting with sktime

Lecture 126 Installing sktime

Lecture 127 Why Forecasting is different from normal machine learning sklearn?

Lecture 128 Different Fitting strategies with sktime

Lecture 129 Different estimators in sktime

Lecture 130 Libraries

Lecture 131 Transforming from weekly to monthly timeseries

Lecture 132 Changing from a normal date to a period date

Lecture 133 Splitting timeseries

Lecture 134 Knearestneighbor

Lecture 135 Deriving the future

Lecture 136 updating the time series with extra 2 years

Lecture 137 Defining a forecast function

Lecture 138 Transformed target Regressor

Lecture 139 Testing the function

Lecture 140 Plotting the results

Lecture 141 Measuring acccuracy

Lecture 142 Cross Validation

Lecture 143 Conclusion

Lecture 144 Assignment

Lecture 145 Assignment Explanation part 1

Lecture 146 assignment explanation part 2

Lecture 147 Assignment explanation part 3

Lecture 148 Assignment part 4

Lecture 149 Assignment part 5

Lecture 150 Assignment Part 6

Lecture 151 Assignment last part

Lecture 152 Summary

Section 12: Hierarchal forecasting

Lecture 153 Introduction

Lecture 154 Levels of a Hierarchy

Lecture 155 Middle-out approach

Lecture 156 Top Down approach

Lecture 157 Forecasting level Usage

Lecture 158 Reconciliation

Lecture 159 Tourism Data

Lecture 160 Making Quarterly series

Lecture 161 Indexing as a Hierarchy

Lecture 162 Fitting Multiple models at once

Lecture 163 Aggregations

Lecture 164 Bottom up Forecasting

Lecture 165 Top Down forecasting

Lecture 166 Comparing Forecasts

Lecture 167 Level 0 Comparison

Lecture 168 Level 0 part 2

Lecture 169 Topdown and weighted least squares

Lecture 170 Final note

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