Practical Forecasting With Excel

Easy Introduction to Excel Forecasting Modeling

What you'll learn

Make analysis of historical data and select most suitable forecasting method for data analysis

Fit forecasting methods to data, and predict future values

Acquire practical skills to implement different forecasting methods in Excel

Learn most effective ways to build practical forecasting models in Excel.

Requirements

No Excel and forecasting experience required. You will learn everything you need

Description

Almost all types of risks can be caused by uncertainty of the future. Two major different ways how we can deal with uncertainty are forecasting of the future based on historical data and simulation technique, basically - Monte Carlo simulations. Sometimes we can use both of the methods together, especially if we are building models with stochastic processes.During this course we are considering how to build forecasting models in Microsoft Excel. Instead of Excel can be used any spreadsheet software, including Google Sheets – won’t be any difference. This forecasting models can be applied to different fields of business administration or even other spears of science and industries, like biology, chemistry, medicine, etc. You use them in project management, budgeting process and different fields of finance, marketing engineering and pricing, including revenue management, etc.The course is designed to help data analysts, all business practitioners, or students to build broad the range of forecasting models in Excel.The course presents an easy and fastest way to build models in Excel. It covers the following topics: simple models, advanced models, regression analysis, seasonality, ARIMA models. Inside of the course you can find explanation of all Excel function which is used for models, so it requires just very basic Excel skills.

Overview

Section 1: Simple forecasting methods

Lecture 1 Moving Averages

Lecture 2 Weighted Moving Averages

Lecture 3 Exponential Smoothing

Lecture 4 First Differences

Lecture 5 Second Differences

Section 2: Regressions

Lecture 6 Linear Regression

Lecture 7 Logarithmic Regression

Lecture 8 Polynomial Regression

Lecture 9 Exponential Regression

Lecture 10 Power Regression

Lecture 11 Multiple Linear Regression

Section 3: More Advance Methods

Lecture 12 Adjusted Exponential Smoothing

Lecture 13 Double Exponential Smoothing

Lecture 14 Double Moving Average

Section 4: Seasonal Models

Lecture 15 Monthly Seasonal Multiplicative Decomposition

Lecture 16 Quarterly Seasonal Multiplicative Decomposition

Lecture 17 Monthly Seasonal Additive Decomposition

Lecture 18 Quarterly Seasonal Additive Decomposition

Lecture 19 Regression with Seasonality

Lecture 20 Winter's 3 parameter model

Section 5: ARIMA models

Lecture 21 Integration: ARIMA (0, 1, 0)

Lecture 22 Auto Regression: ARIMA (1, 0, 0)

Lecture 23 Moving Averages: ARIMA (0, 0, 1)

Lecture 24 ARMA or ARIMA (1, 0, 1)

Lecture 25 ARIMA (1,1,1)

For students and practitioners who require practical forecasting skills