Time Series Analysis And Forecasting With Ms Excel

Learn about a comprehensive framework of Time Series Analysis and Forecasting with MS Excel

What you'll learn

Learn Weighted Average, Exponential Moving Average Analysis and Regression

Simple Forecasting Methods, Simple and Multiple Regression

Time Series Decomposition and Exponential Smoothing

Methods of Forecasting and Steps in Forecasting

Requirements

Prior knowledge of Mathematics and statistics

Description

Time series analysis is a statistical method to analyse the past data within a given duration of time to forecast the future. It comprises of ordered sequence of data at equally spaced interval. To understand the time series data & the analysis let us consider an example. Consider an example of Airline Passenger data. It has the count of passenger over a period of time.Ample of time series data is being generated from a variety of fields. And hence the study time series analysis holds a lot of applications. Let us try to understand the importance of time series analysis in different areas.Field of Economics: Budget studies, census Analysis, etc.Field of Finance: Widely used in the field of finance such as to understand the stock market fluctuations, yield management, understand the market volatility, etc.Social Scientistà: Birth rates or death rates over a period of time and can come with the schemes in their interest.Healthcare: An epidemiologist might be interested in knowing the number of people infected over the past years. Like in the current situation the researchers might be interested in knowing the people affected by the coronavirus over a period of time. Blood pressure traced over a period of time can be used in evaluating a drug.Environmental Science: Environmental time series data can help us explain the rise in temperature over the past few years. Plot shows the temperature data over a period of timeTime series data collected over different points in time breach the assumption of the conventional statistical model as correlation exists between the adjacent data points. This characteristic of the time series data breaches is one of the major assumptions that the adjacent data points are independent and identically distributed. This gives rise to the need of a systematic approach to study the time series data which can help us answer the statistical and mathematical questions that come into the picture due to the time correlation that exists.Time series analysis holds a wide range of applications is it statistics, economics, geography, bioinformatics, neuroscience. The common link between all of them is to come up with a sophisticated technique that can be used to model data over a given period of time where the neighboring information is dependent.In time series, Time is the independent variable and the goal is forecasting.

Overview

Section 1: Introduction

Lecture 1 Introduction to Project

Lecture 2 Forecasting with Excel

Section 2: Scenario

Lecture 3 21st Century in Low Emission Scenario

Lecture 4 21st Century in Low Emission Scenario Continue

Lecture 5 21st Century in Medium Emission Scenario

Lecture 6 21st Century in Medium Emission Scenario Continue

Lecture 7 21st Century in High Emission Scenario

Lecture 8 21st Century in High Emission Scenario Continue

Section 3: Weighted Average

Lecture 9 Calculating Annual Minimum Temperature Average LES

Lecture 10 Weighted Average Maximum Temperature LES

Lecture 11 Weighted Average Minimum Temperature

Lecture 12 Weighted Average Temperature 2A and 2B

Lecture 13 Weighted Average Max Temperature MES

Lecture 14 Weighted Average Minimum Temperature HES

Lecture 15 Weighted Average Max Temperature HES

Section 4: Exponential Moving Average Analysis

Lecture 16 Exponential Average Minimum Temperature Best Scenario

Lecture 17 Exponential Average Maximum Temperature Best Scenario Continue

Lecture 18 Exponential Average Minimum Temperature Normal Scenario

Lecture 19 Exponential Average Maximum Temperature Normal Scenario Continue

Lecture 20 Exponential Average Minimum Temperature Worst Scenario

Lecture 21 Exponential Average Maximum Temperature Worst Scenario Continue

Section 5: Regression

Lecture 22 Correlated MES Min and Max Temperature

Lecture 23 Correlated HES Min and Max Temperature

Lecture 24 Simple Regression LES and HES Max Temperature

Lecture 25 Simple Regression MES and Max Temperature

Lecture 26 Simple Regression HES and Max Temperature

Lecture 27 Multiple Regression Range Prediction

Students, Quantitative and Econometrics Modellers, Financial markets professionals